[ICML 2023] SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models

An Agile RISC-V SoC Design Framework with in-order cores, out-of-order cores, accelerators, and more

OmniQuant is a simple and powerful quantization technique for LLMs.

ArchExplorer: Microarchitecture Exploration Via Bottleneck Analysis

Latency and Memory Analysis of Transformer Models for Training and Inference

OneFlow is a deep learning framework designed to be user-friendly, scalable and efficient.

Utilities for HLS-lab1

Exercises for exploring the Fibertree, Timeloop and Accelergy tools

Linux docker for the DNN accelerator exploration infrastructure composed of Accelergy and Timeloop

A primitive library for neural network

Code from various chapters in OSTEP (http://www.ostep.org)

lab3

Pytorch implementation of BRECQ, ICLR 2021