Multiply-and-Fire (MNF): An Event-driven Sparse Neural Network Accelerator

04/20/2022
by   Miao Yu, et al.
0

Machine learning, particularly deep neural network inference, has become a vital workload for many computing systems, from data centers and HPC systems to edge-based computing. As advances in sparsity have helped improve the efficiency of AI acceleration, there is a continued need for improved system efficiency for both high-performance and system-level acceleration. This work takes a unique look at sparsity with an event (or activation-driven) approach to ANN acceleration that aims to minimize useless work, improve utilization, and increase performance and energy efficiency. Our analytical and experimental results show that this event-driven solution presents a new direction to enable highly efficient AI inference for both CNN and MLP workloads. This work demonstrates state-of-the-art energy efficiency and performance centring on activation-based sparsity and a highly-parallel dataflow method that improves the overall functional unit utilization (at 30 fps). This work enhances energy efficiency over a state-of-the-art solution by 1.46×. Taken together, this methodology presents a novel, new direction to achieve high-efficiency, high-performance designs for next-generation AI acceleration platforms.

READ FULL TEXT

page 7

page 9

research
10/13/2020

High Area/Energy Efficiency RRAM CNN Accelerator with Kernel-Reordering Weight Mapping Scheme Based on Pattern Pruning

Resistive Random Access Memory (RRAM) is an emerging device for processi...
research
09/21/2023

SupeRBNN: Randomized Binary Neural Network Using Adiabatic Superconductor Josephson Devices

Adiabatic Quantum-Flux-Parametron (AQFP) is a superconducting logic with...
research
07/16/2022

S4: a High-sparsity, High-performance AI Accelerator

Exploiting sparsity underlying neural networks has become one of the mos...
research
07/22/2019

A Stochastic-Computing based Deep Learning Framework using Adiabatic Quantum-Flux-Parametron SuperconductingTechnology

The Adiabatic Quantum-Flux-Parametron (AQFP) superconducting technology ...
research
06/03/2020

Stochastic-based Neural Network hardware acceleration for an efficient ligand-based virtual screening

Artificial Neural Networks (ANN) have been popularized in many science a...
research
04/21/2019

Intermittent Learning: On-Device Machine Learning on Intermittently Powered System

In this paper, we introduce the concept of intermittent learning, which ...
research
08/17/2022

Fuse and Mix: MACAM-Enabled Analog Activation for Energy-Efficient Neural Acceleration

Analog computing has been recognized as a promising low-power alternativ...

Please sign up or login with your details

Forgot password? Click here to reset