Tiny ASIC implementation for "The Era of 1-bit LLMs All Large Language Models are in 1.58 Bits" matrix multiplication unit
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Updated
Apr 19, 2024 - Verilog
Tiny ASIC implementation for "The Era of 1-bit LLMs All Large Language Models are in 1.58 Bits" matrix multiplication unit
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.
Template for project1 TPU
This is my senior project, we aim to design a Low-cost-AI-Accelerator based on Google's Tensor Processing Unit.
High-performance systolic-array accelerator for FP32 matrix multiplication in deep learning.
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.
EE599 Accelerated Computing on FPGA
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.
Final project for Digital Systems, IITGN Spring '25
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