Wednesday, 16 March 2022
Our new paper about the ReckOn chip, developed within the SMALL project, has just been presented at the 2022 International Solid-State Circuits Conference. You can read about our chip and its capabilities here.
News
Tuesday, 1 March 2022
Our paper presented at the prestigious ISSCC!
Monday, 10 January 2022
A book chapter on spike-based symbolic computations
Our SMALL consortium is wishing you all a prosperous new year!
Sunday, 26 September 2021
Robot arm control with spiking RNNs - paper accepted to 2021 IEEE/RSJ IROS
Saturday, 25 September 2021
A preprint of our new paper on memristive neuromorphic RNNs is now available
We present a simulation framework of differential-architecture crossbar arrays based on an accurate and comprehensive Phase-Change Memory (PCM) device model, enabling online learning in memristive neuromorphic recurrent neural networks (RNNs). The preprint of our paper is now available here.
Monday, 26 July 2021
Our new research on spike frequency adaptation published on eLife
Creating biologically realistic models for the underlying computations, especially with spiking neurons and for behaviorally relevant integration time spans, is notoriously difficult. We examine the role of spike frequency adaptation in such computations and find that it has a surprisingly large impact. Details on this research are now published on eLife.
Tuesday, 16 February 2021
PCM-trace paper accepted to ISCAS 2021
PCM-trace is our new neuromorphic building block, which exploits the drift behavior of phase-change materials to implement long lasting eligibility traces, a critical ingredient of spike-based learning rules. The paper describing this novel solution has been accepted to the IEEE International Symposium on Circuits and Systems (ISCAS) 2021 (PDF).
Thursday, 28 January 2021
Fast learning in Spiking Neural Networks
In real-world applications, learning has to be fast, relying only on a few training examples. How can we achieve such fast learning in spiking neural networks? We have recently published our resarch on this topic in the preprint Revisiting the role of synaptic plasticity and network dynamics for fast learning in spiking neural networks.
Monday, 30 November 2020
ED-BioRob - A Neuromorphic Robotic Arm
Our bio-inspired solution for spiking motor control of a 4 DoF robotic arm has been described in the recently published paper: ED-BioRob: A Neuromorphic Robotic Arm With FPGA-Based Infrastructure for Bio-Inspired Spiking Motor Controllers.
Friday, 30 October 2020
Published preprint
Published a preprint on spatio-temporal learning in deep neural networks.
Thursday, 1 October 2020
Bidirectional Volatile Signatures of Metal–Oxide Memristors
Results of our comprehensive study for characterizing the relaxation dynamics of TiOx resistive RAM (RRAM) devices within a predefined volatility framework have been published (Part I: Characterization). In part II of this study, we have also presented a modeling framework that can account for RRAM relaxation characteristics (Part II: Modeling).