# A Journey to Low Latency Computer Vision

Engineers have been implementing many basic vision features on computers in the past half century.

Here I want to share some experience about implementing low latency and high performance vision architecture with OpenCV, mainly including instruction set optimization, CUDA programming and multi-threaded programming based on the C++ standard library.

## What is it?

If you have ever known RoboMaster competition which held by DJI, you will quickly understand what we need to do. In a nutshell, it has a following flow:

flowchart LR
subgraph Buffered
Target --> PnP --> Filter --> Compensate
end
Input --> Target
Compensate --> Output

Input obtains data from the outside environment, generally they are camera images and some parameters provided by the serial devices, and Target calculates the position of the target on the 2D image by processing these input data (or already processed loopback data), combined with its position and shape characteristics of the target on the 2D image, the approximate position in real space can be predicted by the PnP algorithm and kalman Filter. Finally, its future position in the real environment is predicted and we take action after the ballistics is Compensated, by sending the position data from the Output serial device to the underlying devices.

The above process is not complicated, but each step is interlocked, while performing the next recognition, it may also need to use the previous recognition results, this is the data loopback. Therefore, the efficiency improvement of each step may bring greater benefits in macro, which is why we have been trying to improve the recognition speed.

Of course, faster recognition speed is only one side of the coin, because faster recognition speed leads to more information, but not all of these data meaningful, because some may contain a lot of noise. To solve that requires a more pronounced temporal dimension, so we need to improve the data structure and the algorithms for processing the additional information.

Moreover, this is not limited to competitions, but in life the visual recognition of roads by self-driving cars, visual inspection robots on assembly lines, etc. All have high requirements for the speed of visual recognition. In today’s world of multiple CPU and multicore computers, we are eager for traditional streaming vision processing algorithms to run better on multiple cores for faster recognition.

## Catching the tail of instructions

Within each task, there are still many micro senses of parallelism or concurrency that are meaningful to increase the processing speed of a single task, which is one of the main reasons that we still choose C or C++ languages thesedays. In addition, choosing the right computing device (CPU or GPU) for different data types or algorithms can often lead to significant speedups.

### SIMD

In the early days of computers, von Neumann architecture computers were able to manipulate only one pair of data by one instruction at a time, which is called “Single Instruction Stream, Single Data Stream”. Obviously, this method of manipulating data is inefficient when dealing with large amounts of data, so what can programmers do if they want to make a single operation work on multiple sets of data? The idea of “Single Instruction Stream, Multiple Data Stream” was introduced, which makes it possible to manipulate multiple sets of data with a single instruction.

graph TD
subgraph SISD
A1[somedata] --> A2[for_each n] --> A3[process] --> A4[result]
end
subgraph SIMD
B1[somedata] --> B2[for_each n/32] --> B3[process] & B4[process] & B5[...] & B6[process] & B7[process]--> B8[result]
end

In the process of acquiring Target, sometimes we need to extract a channel of the RGB image read by the camera and convert it to a binary image. Suppose we need to write a function to threshold a channel into a binary image, and we iterate through the pixel points in _src:

However, a lot of CPU resources are consumed by repeatedly executing the same instructions to determine whether _src[i] is within the threshold range, including the condition instructions of whether the pixel traversal is completed. If there is a large amount of data to process, it is unwise to execute the same instruction operation N times. Modern processors, from the x86 processor’s SSE to AVX and ARM processor’s NEON, almost all have their own SIMD implementations to allow developers to operation multiple sets of data by a single instruction. Here is an example of how NEON instruction set can make the threshold exponentially faster:

Assuming that the number of data to be processed is an integer multiple of 32, the size of the block (the number of elements to be processed each time) can be set to 32, and the 32 elements to be processed each time are divided into two small blocks blk_l and blk_r, each containing 16 elements. and deposit them into dst_, which means the process have complete. Note that if there is not enough data to process, it is often necessary to fill in the data manually. For data that is not an integer multiple of 32, consider taking a 16 elements chunk at the end of the data and processing the remaining elements (less than 16) in the traditional way.

Fortunately, the third-party library Carotene of OpenCV already uses the NEON instruction set to accelerate these basic image processing algorithms. Prefer using Carotene for devices with ARM processors (e.g. threshold functions, OpenCV uses OpenCL implementation by default). Last but not least, note that some compiler optimization flags actually do auto-vectorization for us, like -O1 enables advanced SIMD optimization automatically while compiling.

### GPU

For different computing scenarios, engineers have designed different types of processors for computing, CPU and GPU. For example, CPU requires the generality to handle a variety of data types, and logical judgments introduce a large number of branch jumps and interrupts, all of these make the internal structure of CPU extremely complex, on the other hand, GPU faces a highly uniform type of large-scale data without interdependencies and a pure computing environment without interruptions.

The GPU is actually a SIMD architecture device. Here we take NVIDIA’s GPU as an example and use the CUDA SDK to develop, dividing the gthreshold function into 32 threads in the GPU grid and using threadIdx for thread synchronization to implement the threshold algorithm as described above:

Note that the _gsrc and gdst_ pointers point to data in the GPU’s memory, so how do we use the data in host memory? Assuming that src and dst are one-dimensional array pointers of type unit8_t, similar to the GpuMat of OpenCV, they are usually copied directly form host memory in the following way:

First, use the cudaMalloc function to allocate gsrc and gdst in the GPU memory, exchange data between the Host (CPU) and Device (GPU) with the cudaMemcpy function, wait for processing to complete, copy the processed data back to the host memory, and clean up the memory just allocated in the GPU device.

The above is just a very primitive implementation. In real-world application scenarios, the allocated GPU memory can be reused in most cases, a single step often includes multiple processing methods that need to be run in the GPU, and does not need to be copied back between host and device, or do reallocation. Thus, the differentiation between GPU and CPU poses some difficulties for memory management, which is a problem that must be solved for low-latency vision algorithms. For the memory model between host and device, see the following flowchart:

graph LR
subgraph Host
memory
end
subgraph Device
subgraph Grid
constant_memory
global_memory
subgraph Block
A0[shared_memory]
A3[registers]
A4[registers]
A1 & A2 ---> A0
A1 --> A3
A2 --> A4
end
A0 ---> global_memory
end
end
memory -------> constant_memory & global_memory

The attribute of each variable or function determines its memory location and life cycle:

• int local_var - available in thread, stored in register, life time inside thread
• __device__ int global_var - available in grid, stored in global memory, life time inside application
• __device__ __shared__ int shared_var - available in block, stored in shared memory, life time inside block
• __device__ __constant__ int constant_var - available in grid, stored in constant memory, life time inside application

Maybe someone will mention Zero Copy or Unified Memory, which are indeed good ideas, and here are some examples of the threshold functions regarding them:

• Zero Copy

A feature of the CUDA programming model that enables GPU threads to access host memory directly, which means host and device are using the same physical memory. Note that for CUDA 8.x and below, fixed memory is not pagable, which means that shared memory areas are not contiguous. In a non-coherent environment, pages are not cached and every access to the GPU will use system memory directly, resulting higher latency and lower bandwidth.

• Unified Memory

A component of the CUDA programming model, it defines a managed memory space where host and device can share a common address space and contiguous virtual memory area. It is important to note that even though memory areas are shared via address space (a single pointer to the same virtual area), this does not mean that the same physical memory space is shared. Simply put, the main purpose of using unified memory is not to reduce execution time or improve performance, but to write simpler, more maintainable code.

On traditional PC, zero copy and unified memory are essentially dependent on PCIE bandwidth due to the physical hardware separation of host memory and device memory. On Jetson TX1/TX2 which uses Pascal architecture, that zero copy will incur higher latency and more bandwidth usage, as each shared memory access by the GPU will result in a cache expiration and must be transferred again from host memory. However, Jetson AGX Xavier begins to use the Volta architecture with cache coherency between the CPU and GPU processing units, allowing zero copy programming to be used to share physical memory between the GPU processing units and the host, thus reduced latency overhead and bandwidth usage.

In summary, the proper use of zero copy and unified memory often requires consideration of the hardware conditions, and these memory management models were developed to be compatible with extreme use cases only, not to solve the problem completely. OpenCV provides a number of image processing algorithms that use GPU operations, but still uses the direct copy strategy for memory management. All in all, GPU computation is made for scenarios where it is more suited to computationally intensive programs that spend most of their runtime on register operations (e.g. neural network-based algorithms) rather than on computations that involve frequent memory swapping.

## Simple Asynchrony and Parallelism

Going back to the title, we need to redefine “low latency”. In fact, there are two requirements for low latency: the first is to build Input and Output streams quickly for each cold start, and the second is to have faster processing speed for each processing. The algorithms used to achieve low latency are different for different processing scenarios, so let’s use simple asynchronous and parallel algorithms in our code as much as possible before we start building a concurrent processing architecture.

### Asynchronous

From the first point, here we explore how to quickly build input and output streams. Assuming there are many modules that need to be initialized and self-tested before we start target tracing, and each module takes a certain amount of time to initialize and self-test, so suppose we need to initialize a camera module as an Input source:

During the time while initializing the camera, we are just waiting in the main thread because we are eager to know the result of the initialization before proceeding the initialization of the other modules. In fact most modules are designed to be independent from each other, so there is no need to wait for the initialization of one module to complete before proceeding the others, and the order of execution we would expect is look like this:

sequenceDiagram
participant A as main()
participant B as func()
participant C as func()
activate A
par execution
activate B
activate C
B ->> A: return result
deactivate B
C ->> A: return result
deactivate C
end
deactivate A

So we simply used std::thread to initialize the current module while initializing other modules in different threads, with the initialization results stored in camera_status:

The thread will block at thread::join until initialization is complete. This simple and brute-force design, however, poses some problems. We can’t handle the return value of the function very well, and if we don’t design the timeout throw within camera::init, and the initialization may gets stuck at some step, the thread would block at thread::join forever.

Fortunately, the standard library provides us a way to deal with these problems, which is std::future, future is the result of an asynchronous operation, typically obtained via std::promise::get_future. When the computation is complete, the set_value function of promise is called to set the return value, and the corresponding future::get function blocks the current thread until promise returns the value:

Parameter pack wraps the incoming arguments of lambda expressions, uses std::invoke_result_t to infer the return value type from the incoming arguments of the lambda expressions to determine the declaration type of std::promise, and moves pms to the newly created thread using std::move to the newly created thread to avoid pms being destructed after ftr is returned. If you need to detect if a value has been returned for a period of time, you can use the future::wait_for function. A roughly use is in the following way:

Wait, isn’t that just std::async, so why write it ourselves? Sure, but not exactly true, you can think it as a very condensed draft version. The reasons for not using the standard library’s std::async for example are manifold. Firstly, I had to build a simple example of how async based on std::future is implemented in order to give the reader a better understanding of how it works (it does work, of course). Secondly, the implementation of std::async is different on different platforms, there is no guarantee that the conclusions I abstracted are absolutely correct, because the C++ standard does not specify whether the asynchronous thread is a new thread (GCC/Clang) or reused from a thread pool (MSVC), depending on the compiler’s implementation. But finally, let’s talk about the std::async startup strategy:

• std::launch::async - run a new thread to execute the task asynchronously
• std::launch::deferred - execute the last set task when its result is requested for the first time (lazy computing)

We want the initialization of all modules to be executed in parallel, so we choose the first launch strategy, using std::async with init_camera written roughly as follows:

These are just simple callback functions, and probably don’t need to use something as bulky as std::future. Someone might mention C++ 20’s coroutine, and indeed I considered using its implementation, but after thinking that refactoring the entire code into task<> would be a big job, I would like to leave its implementation in the future, so stay tuned.

### Parallelism

Parallelism, as the name implies, means that multiple tasks are performed simultaneously at the same time, similar to the idea of SIMD in the previous section, but with a significant difference. Imagine that in some processing, there are many candidate targets, which in reality could be contours or rects, for example, defined here for convenience as numbers from 0-9, stored in the container v:

So which targets are the ones I need to consider? We often need to make complex judgments about them, such as whether the target satisfies a certain ratio or contains a certain pattern, such macroscopic parallelism cannot be done directly by instruction set optimization. Assuming that the desired targets are the odd ones here, we get the iterator it that points to these targets via std::partition without copying them, which is easily obtained using modern C++ writes (of course it is also good to use the traditional ways to record the index of the satisfied target elements in the container, which is not demonstrated here):

Is that all that’s needed? Of course not, if we judge them one by one, it’s like having only one queue but having many service windows (assuming the processors are multi-core), which is very inefficient, and we don’t want to use thread or async as huge abstractions for this kind of small, local and non-synchronous processing, so is there an abstraction for this kind of scenario?

That’s why I hava to mention std::execution here, since C++17, this can be used to determine the execution policy of parallel algorithms in the standard library, and there are 3 commonly used execution policies (see execution for more):

• std::execution::seq - keep the original execution order, no parallelism
• std::execution::par - parallelize, structure still has the original execution order
• std::execution::par_unseq - parallelize and vectorize (requires that operations be interleaved, no mutexes are obtained)

Here we use std::execution::par_unseq to parallelize our tasks in the most relaxed way, with a simple modification to the call to std::partition:

If you need to save some temporary data from the judgment process, things get a little more complicated. First, using a parallelized execution strategy causes std::back_inserter to be unsafe (including the push_back and emplace_back operations of the standard library containers), because it is likely that these operations may occur simultaneously. Second, it is also undesirable to preconstruct a temporary container of the same length as v because during parallelized execution, the processing function only knows the item value, but not its index in v. Although it is possible to find the object’s index by some methods such as std::find and std::distance, but this is not friendly to execution efficiency.

In general, we should try to avoid modifying the same variable during parallelized execution, and if this cannot be avoided, first consider whether the data structure can be improved. Suppose the data we need to temporarily save is a random number std::rand, which can be modified as follows:

Next we can consider using locks to protect variables, using std::mutex as an example (I’ll talk the improved atomic lock later), which can be modified as follows:

Here I uses l-reference to avoid copying v. It is important to note that if v changes later, it will also affect e_v, so be careful when using it (smart pointers are not used because they cannot avoid data being copied here).

In fact, most of the algorithms in the standard library algorithm after C++17 has already support parallelized and vectorized execution strategies, depending on how it was used to improve the efficiency of your program. I’d love to talk more about std::accumulate and std::reduce, and std::transform_reduce (there’s a nice demonstration of this in a timing tool I wrote recently Timer), but I’ll save that for later because of words constraints.

## Say goodbye to multicore huddle

In a preliminary discussion of multi-threaded architectures, I would like to mention one of the most commonly used concepts in multi-threaded architectures: the producer and the consumer. In concurrent processing, the producer produces the data to be processed and the consumer processes the data produced by the producer. Producers and consumers can be one-to-one or many-to-many, and we use thread-safe data structures to connect them. Let’s start by looking at the core of our desired architecture (SPSC, which uses a FIFO data structure for synchronization):

sequenceDiagram
participant A as Input
participant B as Queue
participant C as Process
participant D as Queue
participant E as Output
activate A
activate C
activate E
loop execution
B ->> C: pop
D ->> E: pop
Note over C,E: waiting
A ->> B: push
deactivate A
B ->> C: pop
Note over C: processing
C ->> D: push
deactivate C
Note over E: waiting
D ->> E: pop
end
end
deactivate E

This is a very simple and very idealized asynchronous architecture, where Process and Output blocks in the pop process until the data they need is ready (until push occurs), this can be either a constant request or several requests followed by a dormant wait for push to wake up (called wait-free, described in the following section). Assuming an average processing time is tInput >> tProcess >> tOutput for each step, using multiple threads has the following advantages over a single-threaded processing flow:

• Input saves time spent by Process and Output before the second fetch after the first fetch of the data to be processed
• Process saves the time spent by Output after the first processing of data and before the second processing

If it is tInput ≤ tProcess, we often have to take some actions to avoid this due to limitations on the length of the FIFO data structure (computer memory capacity is limited). Suppose we use a double-ended queue std::deque Q for data synchronization, blocking push until pop occurs when Q is full, which is the easiest way to do this, though it will result some lag which is uncertainty. It is also possible to discard expired data, if Q is already full at push occurs, then do push after pop. Although this will break the data continuity to some extent, we can add some parameters to the data structure which needs to be passed to record the number of times this happens and when it happens, so that it is still manageable. But it is not recommended to increase the number of Processes, this may cause the order of the data in Output to be chaotic, because the order in which thread 1 and thread 2 start to be processed to the completion of processing is indeterminate, it needed to increase the coordination between threads using such std::latch or std::barrier, but this goes against the design principles of our architecture.

In general, the ultimate goal of multithreading is not really just make it faster, but make it could be better controlled. Because no matter how much coordination you do, the processing time between each step is uncertain. So we want the data being processed is timestamped, and std::chrono does a great job of helping us do that by making sure the exactly time it takes between each step. In addition, we want multiple threads to be able to perform different tasks at different frequencies, synchronized with each other and without affecting each other, which is crucial for closed-loop control (we use std::hash to check data independence). Imagine in practice, Input sources often have more than one, generally contains images from the camera and the microcontroller sensor data from the serial port, and these data are generated at different frequencies, the frequency of each Process is often generated at the frequency of the smallest one as a benchmark, then how to ensure that each processing, the higher frequency side of the data is in real time? This had to use multiple threads to solve, and often need to use two different FIFO data structures.

As we all know, create or destroy threads will consume many system resources, for streaming data processing, creating a thread every time for processing and destroy it afterwards is not supposed, so the original async writeup is not acceptable. We want simply keep the created thread and use it as a worker, because it could be reused on next processing. First, let’s look at the data structure:

std::string is used to store the worker’s name and std::jthread is used to create the thread. Compared to std::thread, the former jthread will automatically call join while its destructor called due to RAII, and will congested on destruction if it is still not finished. With std::stop_token, it uses like this:

### Data strcuture

When designing data structures for multi-threaded architectures, I often refer to FIFO data structures, because parallelized step-by-step processing can be seen as asynchronous, and the use of FIFO can easily ensure the nature time sequential of the processed data and control the amount of processed data, which can be seen as a liner buffer. The C++ standard library provides us convenient FIFO containers:

• std::queue - accesses only the first and last item (if the container element type does not include a pointer to the previous element), often used when random elements accessing is not required
• std::deque - double-ended queue, used when random elements accessing is needed

Note that the two FIFO containers provided by the C++ standard library are which only guarantee that different elements of the same container can be modified by different threads at the same time, and that const member functions can be called by different threads on the same container at the same time, so it is not absolutely thread-safe and cannot be used for thread synchronization. If you want to use them in a multi-threaded framework to synchronize data, you often need to use them with semaphore or mutex together (or wrap them yourself). I wrote a Queue for MPMC synchronization and data exchange that uses atomic variables, which is lock free and wait free, you can take a look for some references if you’re interested.

Furthermore, even though the data structure of a FIFO can guarantee that items are ordered, there is no guarantee that the time interval between such orderliness is regular, so in a multi-threaded data structure, an absolute timestamp is crucial to tell you when this data was generated and how much time has passed since it was been processed, which can be nicely encapsulated with std::tuple:

The C++ standard library std::chrono provides three different types of clocks, but their implementation depends on the compiler and the hardware architecture, so the time accuracy provided is often not very different (the compiler may reuse the same implementation), see chrono for more detailes (here we use std::chrono::system_clock as an example). Assuming that the data types we want to wrap are std::string and bool, wrapping them with std::deque would be something like this, very simple:

### Semaphore and Atomic

There are many different ways to synchronize threads, such as the traditional way condition_variable, the lightweight atomic and semaphore, which play a very important role in thread synchronization. The concept of semaphores was introduced by the Dutch computer scientist Edsger W. Dijkstra in 1965, but it was not incorporated until the C20 standard library (previously using the C-style semaphore.h). C20 standard library has two semaphores, counting_semaphore and binary_semaphore, the latter would be a special case compared to the former:

You can take semaphonre as a counter, fetching a semaphore decreases the counter and releasing it increases the counter. If a thread tries to acquire a semaphonre when the counter is zero, the thread will be blocked until another thread increases the counter by releasing the semaphonre. To make it easier to understand, I will give a very simple example here, suppose there is a producer that takes 100ms to generate a random number, a consumer that takes 30ms to print out the produced random number, written out like this:

If you know atomic well, you’ll quickly see that the above synchronization using binary semaphore can be done with just two atomic variables, and in fact the binary semaphore works much like this (simple examples):

Perhaps at this point you have completed the implementation and the program is working as you expected, and just when you are relieved and ready to take a break, a problem arises. This is the case where one production corresponds to one consumption, the current consumer does not start the next production until it has get to the data produced by the producer, which simply means that the access to q_with_timestamp by the producer and the consumer does not happen at the same time, and if the maximum capacity of our counting_semaphore is not 1, then we must add a mutex to the common variables between the two threads to protect them, because it is likely that both the producer and the consumer will access it at the same time, which will result a very typical ABA problem.

### Atomic Lock and Memory Order

Thread locks are made for synchronization mechanisms used to forcibly restrict resource access when executing multiple threads. The C++ standard library already provides std::mutex and std::timed_mutex, but since they are OS-level functions in order to ensure strong synchronization between threads, in fact this would be bloated. For our application scenario, we can design a simple ticket lock through atomic, which works much like ordering food in a restaurant service window, where getting a food ticket to wait food is equivalent to locking and returning a food ticket to get food is equivalent to unlocking, so we need two atomic to do this job, m_ticket_out is used to record the distributed “food tickets”, and m_ticket_rec is used to record the recovered “food tickets”, written out roughly like this:

We use alignas in conjunction with std::hardware_destructive_interference_size to avoid automatic memory alignment which result two std::atomic<std::size_t> stored on the same L1 cache line of the CPU, because having them on the same cache line is equivalent to using the same window for both distribution and recovery, which is inefficient for a thread lock that needs to serve for different threads. In addition, we do not want the properties of ticket lock to affect the normal use of atomic, such as using const declarations, so the keyword mutable is used here.

std::hardware_destructive_interference_size is a number suitable as an offset between two objects to avoid false sharing due to different runtime access patterns of different threads. It was proposed in C++17, but until now none of the major compilers except MSVC has support it, so the way we use it on x86_64 or arm64 architectures are often through std::max_align_t by guessing:

On Linux platforms, hardware_destructive_interference_size can also be obtained via sysconf(_SC_LEVEL1_DCACHE_LINESIZE). Next, consider how to design locking and unlocking functions:

First we get the current value of m_ticket_out by fetch_add and make it self-incrementing, if ticket equals m_ticket_rec then return, otherwise we block in the while loop until they are equal. The latter will only happen before the first unlock not happened and the second lock operation executes, thus ensuring the uniqueness and independence of the lock at the same time.

For atomic’s wait and notify_all operations, which were introduced in C++20, are used here to achieve wait-free with as low a latency as possible. It occurs at the first unlock not happened before the second lock operation executes, and after several unsuccessful attempts at the second lock if the first unlock is still not executes. The wait step sleeps and blocks until the first unlock is executed and the second lock would be woken up by notify_all (or the others which is acquiring lock), thus avoiding the performance overhead of being blocked at the acquiring lock step in the while loop.

Finally I would like to talk briefly about one of the important foundations of C++11 in standardized high performance computing, memory order, as it directly affect whether ticket lock works properly and how efficiently it works. On some macro level, multiple threads executing in parallel can be roughly considered as a distributed system. In a distributed system, any communication or even local operation takes some time and even unreliable communication occurs.

sequenceDiagram
par execution
Note over A: v.store(1)
Note over B: v.store(2)
Note over A: v.store(3)
end
par execution
Note over B: v.store(2)
Note over A: v.store(1)
Note over A: v.store(3)
end

If we force the operation of a variable v across multiple threads to be atomic, after any thread operates on v, all other threads sense the change in v synchronously, then the program behaves as a sequential execution for the variable v, but this does not yield any efficiency gain by the introduction of multiple threads, because whether some of the operations to v is synchronized or not, the final result that is essentially exhibited is consistent. What can be done to speed this up properly? The answer is to weaken the inter-process synchronization condition for atomic operations.

Atomic operations use the std::memory_order_seq_cst sequential consistency model by default, where atomic operations satisfy sequential consistency, the compiler does not change the order of operations for optimization, at the cost of generating more CPU instructions to ensure this strong consistency. But ticket lock does not require this strong consistency, we only need to satisfy these requirements:

• For the m_ticket_out.fetch_add operation in lock, it is guaranteed that it is executed sequentially in a single thread and is visible in different threads until the next fetch_add happens
• For the m_ticket_rec.load operation in lock, it is guaranteed that it is executed sequentially in a single thread and is visible in different threads until it is modified by m_ticket_rec.fetch_add operation in unlock happens
• For the m_ticket_rec.wait operation in lock, it is guaranteed to be executed sequentially within a single thread, no explicit order requirements between threads

To sum up, the following changes to the code are made to achieve the final ticket lock we want:

## Not the end

As you can see, this article doesn’t have a step-by-step approach from beginning to end, it only talks about some experience to design a low-latency, high-performance computer vision architecture from different aspects in a note-taking-like way. I haven’t talk deeper on each aspect because this article is long enough, so I’ll do that for further blog posts. Meanwhile, I would like to mention some other optimization ideas, such as the do optimization by expression templates (HCl has written a great article).

My major is actually not computer-science related, and I’ve been learning these in my spare time. I’d be happy if this blog post could help you. The world of C++ is very giant and exciting, and I’m looking forward to C++23’s executors, there is still a lot for me to learn, thanks for reading.