This repository contains the code for the project in the course CS242: Computing at Scale at Harvard. It is a systolic array implementation of the attention mechanism ...
This project implements a 4×4 systolic array accelerator in Verilog and analyzes its execution behavior using a Python-based visualization pipeline. The focus is on building a cycle-accurate hardware ...
In this paper, we first review in detail the basic building blocks of reconfigurable devices, essentially, the field-programmable gate arrays, then we describes a high-speed, reconfigurable Systolic ...
most of an LLM's compute is matrix multiply. nvidia and google built very similar hardware to exploit this. nvidia calls them tensor cores, and google calls them TPUs: in 1978, H.T. Kung and Charles ...
Abstract: In recent years, deep neural networks (DNNs) have experienced rapid development. These DNNs demonstrate significant variations in architecture and scale, creating a substantial demand for ...
Abstract: Convolutional neural network based object detection is an inevitable part of advanced driver assistance systems which has high accuracy and low latency requirements. Considering the high ...
MatX raised $500M to build an LLM-only chip around splittable systolic arrays. compiling the technical info i found on their architecture here: Reiner Pope was efficiency lead for Google PaLM and ...
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