Research and Materials on Hardware implementation of Transformer Model
AutoSA: Polyhedral-Based Systolic Array Compiler
Tiny ASIC implementation for "The Era of 1-bit LLMs All Large Language Models are in 1.58 Bits" matrix multiplication unit
A systolic array simulator for multi-cycle MACs and varying-byte words, with the paper accepted to HPCA 2022.
A general framework for optimizing DNN dataflow on systolic array
Productive and portable performance programming across spatial architectures (FPGAs, etc.) and vector architectures (GPUs, etc.)
Systolic-array based Deep Learning Accelerator generator
This work implements a dynamic programming algorithm for performing local sequence alignment. Through parallelism, it can run 136X times faster than a software running the same algorithm.
Systolic Three Matrix Multiplier for Graph Convolutional Networks using High Level Synthesis
Template for project1 TPU
EE599 Accelerated Computing on FPGA
Working 8x8 systolic array hardware implemented in Xilinx Vivado, operated and controlled in software using Xilinx Vitis
This project is focused on the design and verification of digital logic circuits, particularly targeting chip design using Verilog, SystemVerilog, and SVA. The main objectives included designing modules compliant with industry standards such as APB (Advanced Peripheral Bus), memory systems, and systolic matrix multiplication.
Systolic arrays graphical simulator (SAGS), written in Python.
A general framework for optimizing DNN dataflow on systolic array
SystemVerilog module for matrix multiplication
In this repository you can find all of my projects for Parallel Processing Course when I was in 2nd semester of my master's at SUT.
This is my senior project. Aims to implement the AI accelerator self-test and self-recovery architecture proposed in the paper "STRAIT: Self-Test and Self-Recovery for AI Accelerator". STRAIT is a unified solution that provides self-test, self-diagnosis, and self-recovery functions for systolic array-based AI accelerators.
This is an unfinished test model of CNN, based on cnn.h5 Keras pretrained model EN10/KerasMNIST@4ef71d6/cnn.h5 .
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