A 28-nm Convolutional Neuromorphic Processor Enabling Online Learning with Spike-Based Retinas

• URL: http://arxiv.org/abs/2005.06318v1
• Date: Wed, 13 May 2020 13:47:44 GMT
• Title: A 28-nm Convolutional Neuromorphic Processor Enabling Online Learning with Spike-Based Retinas
• Authors: Charlotte Frenkel, Jean-Didier Legat, David Bol
• Abstract summary: We propose SPOON, a 28-nm event-driven CNN (eCNN) for adaptive edge computing and vision applications. It embeds online learning with only 16.8-% power and 11.8-% area overheads with the biologically-plausible direct random target projection (DRTP) algorithm. With an energy per classification of 313nJ at 0.6V and a 0.32-mm$2$ area for accuracies of 95.3% (on-chip training) and 97.5% (off-chip training) on MNIST, we demonstrate that SPOON reaches the efficiency of conventional machine learning accelerators.
• Score: 1.4425878137951236
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In an attempt to follow biological information representation and organization principles, the field of neuromorphic engineering is usually approached bottom-up, from the biophysical models to large-scale integration in silico. While ideal as experimentation platforms for cognitive computing and neuroscience, bottom-up neuromorphic processors have yet to demonstrate an efficiency advantage compared to specialized neural network accelerators for real-world problems. Top-down approaches aim at answering this difficulty by (i) starting from the applicative problem and (ii) investigating how to make the associated algorithms hardware-efficient and biologically-plausible. In order to leverage the data sparsity of spike-based neuromorphic retinas for adaptive edge computing and vision applications, we follow a top-down approach and propose SPOON, a 28-nm event-driven CNN (eCNN). It embeds online learning with only 16.8-% power and 11.8-% area overheads with the biologically-plausible direct random target projection (DRTP) algorithm. With an energy per classification of 313nJ at 0.6V and a 0.32-mm$^2$ area for accuracies of 95.3% (on-chip training) and 97.5% (off-chip training) on MNIST, we demonstrate that SPOON reaches the efficiency of conventional machine learning accelerators while embedding on-chip learning and being compatible with event-based sensors, a point that we further emphasize with N-MNIST benchmarking.

Related papers

• Analog Spiking Neuron in CMOS 28 nm Towards Large-Scale Neuromorphic Processors [0.8426358786287627]
  In this work, we present a low-power Leaky Integrate-and-Fire neuron design fabricated in TSMC's 28 nm CMOS technology. The fabricated neuron consumes 1.61 fJ/spike and occupies an active area of 34 $mu m2$, leading to a maximum spiking frequency of 300 kHz at 250 mV power supply.
  arXiv  Detail & Related papers  (2024-08-14T17:51:20Z)
• Topology Optimization of Random Memristors for Input-Aware Dynamic SNN [44.38472635536787]
  We introduce pruning optimization for input-aware dynamic memristive spiking neural network (PRIME) Signal representation-wise, PRIME employs leaky integrate-and-fire neurons to emulate the brain's inherent spiking mechanism. For reconfigurability, inspired by the brain's dynamic adjustment of computational depth, PRIME employs an input-aware dynamic early stop policy.
  arXiv  Detail & Related papers  (2024-07-26T09:35:02Z)
• SpikingJelly: An open-source machine learning infrastructure platform for spike-based intelligence [51.6943465041708]
  Spiking neural networks (SNNs) aim to realize brain-inspired intelligence on neuromorphic chips with high energy efficiency. We contribute a full-stack toolkit for pre-processing neuromorphic datasets, building deep SNNs, optimizing their parameters, and deploying SNNs on neuromorphic chips.
  arXiv  Detail & Related papers  (2023-10-25T13:15:17Z)
• Evaluating Spiking Neural Network On Neuromorphic Platform For Human Activity Recognition [2.710807780228189]
  Energy efficiency and low latency are crucial requirements for wearable AI-empowered human activity recognition systems. Spike-based workouts recognition system can achieve a comparable accuracy to popular milliwatt RISC-V bases multi-core processor GAP8 with a traditional neural network.
  arXiv  Detail & Related papers  (2023-08-01T18:59:06Z)
• 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)
• PC-SNN: Supervised Learning with Local Hebbian Synaptic Plasticity based on Predictive Coding in Spiking Neural Networks [1.6172800007896282]
  We propose a novel learning algorithm inspired by predictive coding theory. We show that it can perform supervised learning fully autonomously and successfully as the backprop. This method achieves a favorable performance compared to the state-of-the-art multi-layer SNNs.
  arXiv  Detail & Related papers  (2022-11-24T09:56:02Z)
• Intelligence Processing Units Accelerate Neuromorphic Learning [52.952192990802345]
  Spiking neural networks (SNNs) have achieved orders of magnitude improvement in terms of energy consumption and latency. We present an IPU-optimized release of our custom SNN Python package, snnTorch.
  arXiv  Detail & Related papers  (2022-11-19T15:44:08Z)
• BioGrad: Biologically Plausible Gradient-Based Learning for Spiking Neural Networks [0.0]
  Spiking neural networks (SNN) are delivering energy-efficient, massively parallel, and low-latency solutions to AI problems. To harness these computational benefits, SNN need to be trained by learning algorithms that adhere to brain-inspired neuromorphic principles. We propose a biologically plausible gradient-based learning algorithm for SNN that is functionally equivalent to backprop.
  arXiv  Detail & Related papers  (2021-10-27T00:07:25Z)
• In-Hardware Learning of Multilayer Spiking Neural Networks on a Neuromorphic Processor [6.816315761266531]
  This work presents a spike-based backpropagation algorithm with biological plausible local update rules and adapts it to fit the constraint in a neuromorphic hardware. The algorithm is implemented on Intel Loihi chip enabling low power in- hardware supervised online learning of multilayered SNNs for mobile applications.
  arXiv  Detail & Related papers  (2021-05-08T09:22:21Z)
• Optimizing Memory Placement using Evolutionary Graph Reinforcement Learning [56.83172249278467]
  We introduce Evolutionary Graph Reinforcement Learning (EGRL), a method designed for large search spaces. We train and validate our approach directly on the Intel NNP-I chip for inference. We additionally achieve 28-78% speed-up compared to the native NNP-I compiler on all three workloads.
  arXiv  Detail & Related papers  (2020-07-14T18:50:12Z)
• Rectified Linear Postsynaptic Potential Function for Backpropagation in Deep Spiking Neural Networks [55.0627904986664]
  Spiking Neural Networks (SNNs) usetemporal spike patterns to represent and transmit information, which is not only biologically realistic but also suitable for ultra-low-power event-driven neuromorphic implementation. This paper investigates the contribution of spike timing dynamics to information encoding, synaptic plasticity and decision making, providing a new perspective to design of future DeepSNNs and neuromorphic hardware systems.
  arXiv  Detail & Related papers  (2020-03-26T11:13:07Z)

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.