Neuromorphic analog circuits for robust on-chip always-on learning in spiking neural networks
METADATA ONLY
Loading...
Author / Producer
Date
2023
Publication Type
Conference Paper
ETH Bibliography
yes
Citations
Altmetric
METADATA ONLY
Data
Rights / License
Abstract
Mixed-signal neuromorphic systems represent a promising solution for solving extreme-edge computing tasks without relying on external computing resources. Their spiking neural network circuits are optimized for processing sensory data on-line in continuous-time. However, their low precision and high variability can severely limit their performance. To address this issue and improve their robustness to inhomogeneities and noise in both their internal state variables and external input signals, we designed on-chip learning circuits with short-term analog dynamics and long-term tristate discretization mechanisms. An additional hysteretic stop-learning mechanism is included to improve stability and automatically disable weight updates when necessary, to enable continuous always-on learning. We designed a spiking neural network with these learning circuits in a prototype chip using a 180 nm CMOS technology. Simulation and silicon measurement results from the prototype chip are presented. These circuits enable the construction of large-scale spiking neural networks with online learning capabilities for real-world edge computing tasks.
Permanent link
Publication status
published
Editor
Book title
2023 IEEE 5th International Conference on Artificial Intelligence Circuits and Systems (AICAS)
Journal / series
Volume
Pages / Article No.
10168620
Publisher
IEEE
Event
5th International Conference on Artificial Intelligence Circuits and Systems (AICAS 2023)
Edition / version
Methods
Software
Geographic location
Date collected
Date created
Subject
Always-on learning; Edge-computing; On-chip learning online; SNN; Hysteresis; Tristability
Organisational unit
09699 - Indiveri, Giacomo / Indiveri, Giacomo
Notes
Funding
871737 - BEOL technology platform based on ferroelectric synaptic devices for advanced neuromorphic processors (EC)