HEAM: High-Efficiency Approximate Multiplier Optimization for Deep Neural Networks

Related tags

Deep LearningHEAM
Overview

Approximate Multiplier by HEAM

What's HEAM?

  • HEAM is a general optimization method to generate high-efficiency approximate multipliers for specific applications.
  • This project contains an 8x8 unsigned approximate multiplier based on HEAM for Deep Neural Network (DNN) accelerators and the corresponding Design Compiler(DC) script. Besides, the exact WallaceTree multiplier is included for comparison.

Optimization Procedure of the 8×8 Unsigned Approximate Multiplier

How to compile them?

Make sure that you have installed Design Compiler(DC) and prepared your library files.

compile approximate_multiplier.v

  • step 1: set TOP_LEVEL, all_src, and TOP in scripts/top.tcl at line 1, line 11, and line 15 respectively:
set TOP_LEVEL approximate_multiplier
set all_src "approximate_multiplier.v"
set TOP approximate_multiplier
  • step 2: run commands in terminal:
dc_shell
source scripts/top.tcl

compile wallacetree.v

  • step 1: set TOP_LEVEL, all_src, and TOP in scripts/top.tcl at line 1, line 11, and line 15 respectively:
set TOP_LEVEL wallacetree
set all_src "wallacetree.v"
set TOP wallacetree
  • step 2: run commands in terminal:
dc_shell
source scripts/top.tcl

Experiments of the Approximate Multiplier and the Exact WallaceTree multiplier on Design Compiler(DC) in 3Ghz with a 7-nm Predictive Process Design Kit (PDK) Called the ASAP7 PDK[1]

Ours WallaceTree Reduction
Area ( μm * μm ) 17.52516 42.98184 59.23%
Power ( μW ) 76.2003 151.9432 49.85%

Future

  1. add several reproduced approximate multipliers for comparison;
  2. add DNNs accelerators results.

Reference

[1] Clark, Lawrence T., et al. "ASAP7: A 7-nm finFET predictive process design kit." Microelectronics Journal 53 (2016): 105-115.

Owner
Architecture for Reconfigurable Computing
Quickly comparing your image classification models with the state-of-the-art models (such as DenseNet, ResNet, ...)

Image Classification Project Killer in PyTorch This repo is designed for those who want to start their experiments two days before the deadline and ki

349 Dec 08, 2022
NeoPlay is the project dedicated to ESport events.

NeoPlay is the project dedicated to ESport events. On this platform users can participate in tournaments with prize pools as well as create their own tournaments.

3 Dec 18, 2021
DCGAN-tensorflow - A tensorflow implementation of Deep Convolutional Generative Adversarial Networks

DCGAN in Tensorflow Tensorflow implementation of Deep Convolutional Generative Adversarial Networks which is a stabilize Generative Adversarial Networ

Taehoon Kim 7.1k Dec 29, 2022
StyleGAN2 with adaptive discriminator augmentation (ADA) - Official TensorFlow implementation

StyleGAN2 with adaptive discriminator augmentation (ADA) — Official TensorFlow implementation Training Generative Adversarial Networks with Limited Da

NVIDIA Research Projects 1.7k Dec 29, 2022
ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator

ONNX Runtime is a cross-platform inference and training machine-learning accelerator. ONNX Runtime inference can enable faster customer experiences an

Microsoft 8k Jan 04, 2023
Official repository for the NeurIPS 2021 paper Get Fooled for the Right Reason: Improving Adversarial Robustness through a Teacher-guided curriculum Learning Approach

Get Fooled for the Right Reason Official repository for the NeurIPS 2021 paper Get Fooled for the Right Reason: Improving Adversarial Robustness throu

Sowrya Gali 1 Apr 25, 2022
Sparse Physics-based and Interpretable Neural Networks

Sparse Physics-based and Interpretable Neural Networks for PDEs This repository contains the code and manuscript for research done on Sparse Physics-b

28 Jan 03, 2023
🔎 Super-scale your images and run experiments with Residual Dense and Adversarial Networks.

Image Super-Resolution (ISR) The goal of this project is to upscale and improve the quality of low resolution images. This project contains Keras impl

idealo 4k Jan 08, 2023
Pytorch implement of 'Unmixing based PAN guided fusion network for hyperspectral imagery'

Pgnet There's a improved version compared with the publication in Tgrs with the modification in the deduction of the PDIN block: https://arxiv.org/abs

5 Jul 01, 2022
Unsupervised 3D Human Mesh Recovery from Noisy Point Clouds

Unsupervised 3D Human Mesh Recovery from Noisy Point Clouds Xinxin Zuo, Sen Wang, Minglun Gong, Li Cheng Prerequisites We have tested the code on Ubun

41 Dec 12, 2022
Deep Q Learning with OpenAI Gym and Pokemon Showdown

pokemon-deep-learning An openAI gym project for pokemon involving deep q learning. Made by myself, Sam Little, and Layton Webber. This code captures g

2 Dec 22, 2021
Alphabetical Letter Recognition

DecisionTrees-Image-Classification Alphabetical Letter Recognition In these demo we are using "Decision Trees" Our database is composed by Learning Im

Mohammed Firass 4 Nov 30, 2021
Doods2 - API for detecting objects in images and video streams using Tensorflow

DOODS2 - Return of DOODS Dedicated Open Object Detection Service - Yes, it's a b

Zach 101 Jan 04, 2023
Data and extra materials for the food safety publications classifier

Data and extra materials for the food safety publications classifier The subdirectories contain detailed descriptions of their contents in the README.

1 Jan 20, 2022
3D-Transformer: Molecular Representation with Transformer in 3D Space

3D-Transformer: Molecular Representation with Transformer in 3D Space

55 Dec 19, 2022
Colab notebook for openai/glide-text2im.

GLIDE text2im on Colab This repository provides a Colab notebook to produce images conditioned on text prompts with GLIDE [1]. Usage Run text2im.ipynb

Wok 19 Oct 19, 2022
Minimal implementation of Denoised Smoothing: A Provable Defense for Pretrained Classifiers in TensorFlow.

Denoised-Smoothing-TF Minimal implementation of Denoised Smoothing: A Provable Defense for Pretrained Classifiers in TensorFlow. Denoised Smoothing is

Sayak Paul 19 Dec 11, 2022
Software associated to AAAI paper "Planning with Biological Neurons and Synapses"

jBrain Software associated with the AAAI 2022 paper Francesco D'Amore, Daniel Mitropolsky, Pierluigi Crescenzi, Emanuele Natale, Christos H. Papadimit

Pierluigi Crescenzi 1 Apr 10, 2022
Just Randoms Cats with python

Random-Cat Just Randoms Cats with python.

OriCode 2 Dec 21, 2021
Offline Multi-Agent Reinforcement Learning Implementations: Solving Overcooked Game with Data-Driven Method

Overcooked-AI We suppose to apply traditional offline reinforcement learning technique to multi-agent algorithm. In this repository, we implemented be

Baek In-Chang 14 Sep 16, 2022