Public repository of public code on GitHub for the initial implementation tests of the Perceptron in C++. Soon, I will make more commits with the implementation reduced to C. Afterwards, I will explore libraries, frameworks, and tools in C/C++ for AI development, and look for optimization possibilities using AVX, MMX, SSE, SIMD, etc., with NASM, MASM, and/or C Inline Assembly.

Project IA-Perceptron

(c0d3d by h0ffy)
https://github.com/h0ffy/IA-Perceptron

(Forked from kierank)
https://github.com/JennyLabForks/FFMPEG-ASM-Lessons

ASM Lessons FFMPEG

(kierank on GitHub)
https://github.com/kierank

(Original Repository)

https://github.com/FFmpeg/asm-lessons

My Two Date Inna Day

Optimizing Perceptron Performance through: Code Conversion & Advanced Optimization Techniques

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This research PoC focuses on optimizing the performance of a Perceptron model implemented in C++. The main objective is to achieve significant improvements in execution speed and model efficiency through code conversion and the application of low-level optimization techniques.

Conversion to C and Exploitation of SIMD Instructions

The first stage of optimization involves converting the source code from C++ to C. This conversion facilitates the implementation of SIMD (Single Instruction, Multiple Data) instructions, such as those provided by the AVX, SSE, and MMX extensions. These extensions allow parallel operations on multiple data simultaneously, which can result in a considerable increase in performance, especially in tasks involving vector or matrix operations, such as those found in the Perceptron algorithm.

Code Analysis and Selection of Optimization Techniques

A comprehensive analysis of the Perceptron code will be performed to identify bottlenecks and areas susceptible to optimization. This analysis will include the identification of critical loops, CPU-intensive operations, and memory access patterns that can be improved by applying optimization techniques such as:

• Vectorization: Utilizing SIMD instructions to parallelize operations.
• Loop unrolling: Reducing loop iteration overhead by replicating the loop body.
• Memory access optimization: Improving data locality and minimizing memory accesses.
• Dead code elimination: Removing code that does not contribute to the final result.

Integration of Optimizations and Performance Evaluation:

The selected optimization techniques will be carefully integrated into the Perceptron code. Profiling and benchmarking tools will be used to measure the impact of each optimization on the model's performance. Special attention will be paid to reducing execution time, optimizing memory usage, and energy efficiency.

Inspiration from ffmpeg

The project will draw inspiration from optimization examples extracted from ffmpeg, a software library widely recognized for its high performance and extensive use of low-level optimization techniques. The optimization strategies employed in ffmpeg will be analyzed to identify best practices and adapt them to the context of the Perceptron.

Expected Results and Benefits

This research project is expected to achieve substantial improvements in the performance of the Perceptron model. The results obtained will be documented in detail and shared with the community, providing valuable guidance for optimizing other artificial intelligence projects. Furthermore, this project will contribute to a better understanding of code optimization techniques and their application in the development of machine learning algorithms.

As he explains, the example code simply uses 'xor r0q, r0q' to generate 0. This is reminiscent of shellcode programming in an ASCII space where returning 0 is not an option. I am also aware of another option that I will test for speed to determine if it functions similarly to 'r0q'. If that is not possible, then 't0q' (xor t0q, t0q) could be an alternative. I need test it. Other example is m4q,m5q.. But now I not test it. In addition, it is already possible to do a significant percentage of the Perceptron code, since it allows for double loops in a fairly high intensity optimisation. We will then proceed to analyse the most interesting parts of the article, after which we will implement the first versions in ASM and optimise them. 

So according to what he tells us. With that code example simply with "xor r0q,r0q" as a method of generating 0. This reminds me of shellcode..

Other implementation of SSE

He talk about percents of computer compability instructions around the world...

Testing first MMX and float optimization

Now.... I code fast.... I need sleep. Commit code and today solve errerors and others... :P. Im my test start with float and MMX optimizations... Next SSE ;)

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