Halutmatmul

Algorithmic CI

Hardware CI

General Information

• Based on MADDness/Bolt.
• More information about the base project is here
• arXiv paper link

This repo is used for the algorithmic exploration. I will try to update this repo with as much hardware information as I am allowed to publish.

Install

# install conda environment & activate
conda env create -f environment_gpu.yml
conda activate halutmatmul

# IIS prefixed env
conda env create -f environment_gpu.yml --prefix /scratch/janniss/conda/halutmatmul_gpu

# install CLI
./scripts/install-cli.sh

# now use CLI with
halut --help

# or without install
./halut --help

Hackernews mention (comments only) and discussion

• HN: Bolt: Faster matrix and vector operations that run on compressed data

Hardware OpenROAD flow results

All Designs	ASAP7	NanGate45
All Report	All	All
History	History	History

Total Circuit (M=2)

halut_matmul	ASAP7	NanGate45
Area [μm^2]	9643.6787	140647.7656
Freq [Mhz]	666.7	333.3
GE	110.238 kGE	176.25 kGE
Std Cell [#]	68186	68994
Voltage [V]	0.77	1.1
Util [%]	45.0	59.2
TNS	-1086.59	-0.31
Clock Net
Gallery	Gallery Viewer	Gallery Viewer
Metrics	Metrics Viewer	Metrics Viewer
Report	Report Viewer	Report Viewer

Encoder

halut_encoder_4	ASAP7	NanGate45
Area [μm^2]	4844.5405	69711.9531
Freq [Mhz]	666.7	333.3
GE	55.378 kGE	87.358 kGE
Std Cell [#]	34334	33746
Voltage [V]	0.77	1.1
Util [%]	45.0	58.7
TNS	0.0	0.0
Clock Net
Gallery	Gallery Viewer	Gallery Viewer
Metrics	Metrics Viewer	Metrics Viewer
Report	Report Viewer	Report Viewer

Decoder

halut_decoder	ASAP7	NanGate45
Area [μm^2]	4749.8286	68923.7891
Freq [Mhz]	666.7	333.3
GE	54.296 kGE	86.37 kGE
Std Cell [#]	33709	34395
Voltage [V]	0.77	1.1
Util [%]	44.4	58.9
TNS	-11340.5098	-0.66
Clock Net
Gallery	Gallery Viewer	Gallery Viewer
Metrics	Metrics Viewer	Metrics Viewer
Report	Report Viewer	Report Viewer

Progress Slides

• Week 11 slides
• Weekly updated slides

CUDA kernels

• Encode Kernel
• Decode Kernel

I am aware that there is still a lot that could be optimized here (warp etc.), but it was only developed for fast analysis

Results

Caveats: No retraining and fine-tuning done yet!

Single Layer replacement with C=32 and K=16

LeViT (Source)

SOTA Vision Transformer on ImageNet 1K

ResNet-50 (only interesting layers in analysis)

Legacy Classifier on ImageNet 1K

Depthwise seperable CNN

on Google Speech v2

C, K and encoding_algorithm parameter sweep for ResNet-50

• Data visualizer be sure to select ResNet-50 layers layerX.X.convX

Offline learning convergence on ResNet-50

The goal was to find out how much offline training data is needed to get the maximum accuracy.

Formalism

Some definitions about the forward path.

Encode kernel

Read and accumulate LUTs kernel

Links

• Addendum