DYNAP-SE2: a scalable multi-core dynamic neuromorphic asynchronous
spiking neural network processor
- URL: http://arxiv.org/abs/2310.00564v2
- Date: Fri, 10 Nov 2023 16:46:37 GMT
- Title: DYNAP-SE2: a scalable multi-core dynamic neuromorphic asynchronous
spiking neural network processor
- Authors: Ole Richter, Chenxi Wu, Adrian M. Whatley, German K\"ostinger, Carsten
Nielsen, Ning Qiao and Giacomo Indiveri
- Abstract summary: We present a brain-inspired platform for prototyping real-time event-based Spiking Neural Networks (SNNs)
The system proposed supports the direct emulation of dynamic and realistic neural processing phenomena such as short-term plasticity, NMDA gating, AMPA diffusion, homeostasis, spike frequency adaptation, conductance-based dendritic compartments and spike transmission delays.
The flexibility to emulate different biologically plausible neural networks, and the chip's ability to monitor both population and single neuron signals in real-time, allow to develop and validate complex models of neural processing for both basic research and edge-computing applications.
- Score: 2.9175555050594975
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: With the remarkable progress that technology has made, the need for
processing data near the sensors at the edge has increased dramatically. The
electronic systems used in these applications must process data continuously,
in real-time, and extract relevant information using the smallest possible
energy budgets. A promising approach for implementing always-on processing of
sensory signals that supports on-demand, sparse, and edge-computing is to take
inspiration from biological nervous system. Following this approach, we present
a brain-inspired platform for prototyping real-time event-based Spiking Neural
Networks (SNNs). The system proposed supports the direct emulation of dynamic
and realistic neural processing phenomena such as short-term plasticity, NMDA
gating, AMPA diffusion, homeostasis, spike frequency adaptation,
conductance-based dendritic compartments and spike transmission delays. The
analog circuits that implement such primitives are paired with a low latency
asynchronous digital circuits for routing and mapping events. This asynchronous
infrastructure enables the definition of different network architectures, and
provides direct event-based interfaces to convert and encode data from
event-based and continuous-signal sensors. Here we describe the overall system
architecture, we characterize the mixed signal analog-digital circuits that
emulate neural dynamics, demonstrate their features with experimental
measurements, and present a low- and high-level software ecosystem that can be
used for configuring the system. The flexibility to emulate different
biologically plausible neural networks, and the chip's ability to monitor both
population and single neuron signals in real-time, allow to develop and
validate complex models of neural processing for both basic research and
edge-computing applications.
Related papers
- Adaptive Robotic Arm Control with a Spiking Recurrent Neural Network on a Digital Accelerator [41.60361484397962]
We present an overview of the system, and a Python framework to use it on a Pynq ZU platform.
We show how the simulated accuracy is preserved with a peak performance of 3.8M events processed per second.
arXiv Detail & Related papers (2024-05-21T14:59:39Z) - Neuromorphic analog circuits for robust on-chip always-on learning in
spiking neural networks [1.9809266426888898]
Mixed-signal neuromorphic systems represent a promising solution for solving extreme-edge computing tasks.
Their spiking neural network circuits are optimized for processing sensory data on-line in continuous-time.
We design on-chip learning circuits with short-term analog dynamics and long-term tristate discretization mechanisms.
arXiv Detail & Related papers (2023-07-12T11:14:25Z) - Contrastive-Signal-Dependent Plasticity: Forward-Forward Learning of
Spiking Neural Systems [73.18020682258606]
We develop a neuro-mimetic architecture, composed of spiking neuronal units, where individual layers of neurons operate in parallel.
We propose an event-based generalization of forward-forward learning, which we call contrastive-signal-dependent plasticity (CSDP)
Our experimental results on several pattern datasets demonstrate that the CSDP process works well for training a dynamic recurrent spiking network capable of both classification and reconstruction.
arXiv Detail & Related papers (2023-03-30T02:40:28Z) - ETLP: Event-based Three-factor Local Plasticity for online learning with
neuromorphic hardware [105.54048699217668]
We show a competitive performance in accuracy with a clear advantage in the computational complexity for Event-Based Three-factor Local Plasticity (ETLP)
We also show that when using local plasticity, threshold adaptation in spiking neurons and a recurrent topology are necessary to learntemporal patterns with a rich temporal structure.
arXiv Detail & Related papers (2023-01-19T19:45:42Z) - Fluid Batching: Exit-Aware Preemptive Serving of Early-Exit Neural
Networks on Edge NPUs [74.83613252825754]
"smart ecosystems" are being formed where sensing happens concurrently rather than standalone.
This is shifting the on-device inference paradigm towards deploying neural processing units (NPUs) at the edge.
We propose a novel early-exit scheduling that allows preemption at run time to account for the dynamicity introduced by the arrival and exiting processes.
arXiv Detail & Related papers (2022-09-27T15:04:01Z) - PulseDL-II: A System-on-Chip Neural Network Accelerator for Timing and
Energy Extraction of Nuclear Detector Signals [3.307097167756987]
We introduce PulseDL-II, a system-on-chip (SoC) specially designed for applications of event feature (time, energy, etc.) extraction from pulses with deep learning.
The proposed system achieved 60 ps time resolution and 0.40% energy resolution with online neural network inference at signal to noise ratio (SNR) of 47.4 dB.
arXiv Detail & Related papers (2022-09-02T08:52:21Z) - POPPINS : A Population-Based Digital Spiking Neuromorphic Processor with
Integer Quadratic Integrate-and-Fire Neurons [50.591267188664666]
We propose a population-based digital spiking neuromorphic processor in 180nm process technology with two hierarchy populations.
The proposed approach enables the developments of biomimetic neuromorphic system and various low-power, and low-latency inference processing applications.
arXiv Detail & Related papers (2022-01-19T09:26:34Z) - Multi-Tones' Phase Coding (MTPC) of Interaural Time Difference by
Spiking Neural Network [68.43026108936029]
We propose a pure spiking neural network (SNN) based computational model for precise sound localization in the noisy real-world environment.
We implement this algorithm in a real-time robotic system with a microphone array.
The experiment results show a mean error azimuth of 13 degrees, which surpasses the accuracy of the other biologically plausible neuromorphic approach for sound source localization.
arXiv Detail & Related papers (2020-07-07T08:22:56Z) - Ultra-Low-Power FDSOI Neural Circuits for Extreme-Edge Neuromorphic
Intelligence [2.6199663901387997]
In-memory computing mixed-signal neuromorphic architectures provide promising ultra-low-power solutions for edge-computing sensory-processing applications.
We present a set of mixed-signal analog/digital circuits that exploit the features of advanced Fully-Depleted Silicon on Insulator (FDSOI) integration processes.
arXiv Detail & Related papers (2020-06-25T09:31:29Z) - Flexible Transmitter Network [84.90891046882213]
Current neural networks are mostly built upon the MP model, which usually formulates the neuron as executing an activation function on the real-valued weighted aggregation of signals received from other neurons.
We propose the Flexible Transmitter (FT) model, a novel bio-plausible neuron model with flexible synaptic plasticity.
We present the Flexible Transmitter Network (FTNet), which is built on the most common fully-connected feed-forward architecture.
arXiv Detail & Related papers (2020-04-08T06:55:12Z) - Structural plasticity on an accelerated analog neuromorphic hardware
system [0.46180371154032884]
We present a strategy to achieve structural plasticity by constantly rewiring the pre- and gpostsynaptic partners.
We implemented this algorithm on the analog neuromorphic system BrainScaleS-2.
We evaluated our implementation in a simple supervised learning scenario, showing its ability to optimize the network topology.
arXiv Detail & Related papers (2019-12-27T10:15:58Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.