NARS: Neuromorphic Acceleration through Register-Streaming Extensions on RISC-V Cores
METADATA ONLY
Loading...
Author / Producer
Date
2024-05
Publication Type
Conference Paper
ETH Bibliography
yes
Citations
Altmetric
METADATA ONLY
Data
Rights / License
Abstract
Spiking Neural Networks (SNNs) have emerged as a promising bio-inspired solution to address the need for low-latency, energy-efficient artificial intelligence systems. SNNs pose a challenge to traditional CPUs, GPUs and neural network accelerators due to their inherent sparsity, spike-based communication between neurons and complex activation functions. Many neuromorphic accelerators have been developed to handle this complex workload, but these systems are often designed solely to accelerate spiking networks, resulting in huge area costs and a lack of flexibility. We address this problem by proposing a novel mapping methodology for Convolutional SNNs (S-CNNs) on a general-purpose open-source RISC-V core equipped with Indirection Streaming Semantic Registers, a lightweight ISA extension for accelerating sparse-dense linear algebra. NARS is the first work to map S-CNNs in a classical sparse-dense algebra paradigm. Our methodology shows that it is possible to achieve speedups on S-CNNs microkernels with sparsity degrees compatible with state-of-the-art S-CNNs ranging from 4.33× to 10.23× on a dense baseline and from 1.12× to 2.66× on a optimized dense implementation.
Permanent link
Publication status
published
External links
Editor
Book title
CF '24 Companion: Proceedings of the 21st ACM International Conference on Computing Frontiers: Workshops and Special Sessions
Journal / series
Volume
Pages / Article No.
79 - 82
Publisher
Association for Computing Machinery
Event
21st ACM International Conference on Computing Frontiers (CF 2024)
Edition / version
Methods
Software
Geographic location
Date collected
Date created
Subject
Neuromorphic Computing; Sparse Linear Algebra; RISC-V
Organisational unit
03996 - Benini, Luca / Benini, Luca