XPost: comp.lang.vhdl, comp.arch.fpga

Pasted below is an overview/abstract, and you will find more information (including a paper, demo video, statistics, slides, and source code) at
the following GitHub repository:

https://github.com/Thraetaona/Innervator

------------------------------------------------------------------------ Artificial intelligence ("AI") is deployed in various applications, from
noise cancellation to image recognition, but AI-based products often
come with high hardware and electricity costs; this makes them
inaccessible for consumer devices and small-scale edge electronics.
Inspired by biological brains, deep neural networks ("DNNs") are modeled
using mathematical formulae, yet general-purpose processors treat otherwise-parallelizable AI algorithms as step-by-step sequential logic.
 In contrast, programmable logic devices ("PLDs") can be customized to
the specific parameters of a trained DNN, thereby ensuring data-tailored computation and algorithmic parallelism at the register-transfer level. Furthermore, a subgroup of PLDs, field-programmable gate arrays
("FPGAs"), are dynamically reconfigurable.  So, to improve AI runtime performance, I designed and open-sourced my hardware compiler:
Innervator.  Written entirely in VHDL-2008, Innervator takes any DNN's metadata and parameters (e.g., number of layers, neurons per layer, and
their weights/biases), generating its synthesizable FPGA hardware
description with the appropriate pipelining and batch processing.
Innervator is entirely portable and vendor-independent.  As a proof of concept, I used Innervator to implement a sample 8x8-pixel handwritten digit-recognizing neural network in a low-cost AMD Xilinx Artix-7(TM)
FPGA @ 100 MHz.  With 3 pipeline stages and 2 batches at about 67% LUT utilization, the Network achieved ~7.12 GOP/s, predicting the output in
630 ns and under 0.25 W of power.  In comparison, an Intel(R) Core(TM) i7-12700H CPU @ 4.70 GHz would take 40,000-60,000 ns at 45 to 115 W. Ultimately, Innervator's hardware-accelerated approach bridges the
inherent mismatch between current AI algorithms and the general-purpose
digital hardware they run on. ------------------------------------------------------------------------

(Forgot to cross-post to c.a.fpga and c.a.embedded; adding them now.)

--- SoupGate-Win32 v1.05
* Origin: fsxNet Usenet Gateway (21:1/5)