Gokul Krishnan's repositories
A comprehensive tool that allows for system-level performance estimation of chiplet-based In-Memory computing (IMC) architectures.
Repository for the tools and non-commercial data used for the "Accelerator wall" paper.
Keras code and weights files for popular deep learning models.
GPU and CPU measurements for ML inference workloads for power, latency and throughput
This is a collection of works on neural networks and neural accelerators.
[ASPLOS 2019] PUMA-simulator provides a detailed simulation model of a dataflow architecture built with NVM (non-volatile memory), and runs ML models compiled using the puma compiler.
Signal forecasting with a Sequence-to-Sequence (seq2seq) Recurrent Neural Network (RNN) model in TensorFlow - Guillaume Chevalier
SiliconCompiler is an open source build system that automates translation from source code to silicon.