Ultra-low-power Image Classification on Neuromorphic Hardware

• URL: http://arxiv.org/abs/2309.16795v2
• Date: Fri, 21 Jun 2024 22:34:46 GMT
• Title: Ultra-low-power Image Classification on Neuromorphic Hardware
• Authors: Gregor Lenz, Garrick Orchard, Sadique Sheik,
• Abstract summary: Spiking neural networks (SNNs) promise ultra-low-power applications by exploiting temporal and spatial sparsity. Training such SNNs using backpropagation through time for vision tasks that rely mainly on spatial features is computationally costly. We propose a temporal ANN-to-SNN conversion method, which we call Quartz, that is based on the time to first spike.
• Score: 3.784976081087904
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Spiking neural networks (SNNs) promise ultra-low-power applications by exploiting temporal and spatial sparsity. The number of binary activations, called spikes, is proportional to the power consumed when executed on neuromorphic hardware. Training such SNNs using backpropagation through time for vision tasks that rely mainly on spatial features is computationally costly. Training a stateless artificial neural network (ANN) to then convert the weights to an SNN is a straightforward alternative when it comes to image recognition datasets. Most conversion methods rely on rate coding in the SNN to represent ANN activation, which uses enormous amounts of spikes and, therefore, energy to encode information. Recently, temporal conversion methods have shown promising results requiring significantly fewer spikes per neuron, but sometimes complex neuron models. We propose a temporal ANN-to-SNN conversion method, which we call Quartz, that is based on the time to first spike (TTFS). Quartz achieves high classification accuracy and can be easily implemented on neuromorphic hardware while using the least amount of synaptic operations and memory accesses. It incurs a cost of two additional synapses per neuron compared to previous temporal conversion methods, which are readily available on neuromorphic hardware. We benchmark Quartz on MNIST, CIFAR10, and ImageNet in simulation to show the benefits of our method and follow up with an implementation on Loihi, a neuromorphic chip by Intel. We provide evidence that temporal coding has advantages in terms of power consumption, throughput, and latency for similar classification accuracy. Our code and models are publicly available.

Related papers

• Accurate Mapping of RNNs on Neuromorphic Hardware with Adaptive Spiking Neurons [2.9410174624086025]
  We present a $SigmaDelta$-low-pass RNN (lpRNN) for mapping rate-based RNNs to spiking neural networks (SNNs) An adaptive spiking neuron model encodes signals using $SigmaDelta$-modulation and enables precise mapping. We demonstrate the implementation of the lpRNN on Intel's neuromorphic research chip Loihi.
  arXiv  Detail & Related papers  (2024-07-18T14:06:07Z)
• Braille Letter Reading: A Benchmark for Spatio-Temporal Pattern Recognition on Neuromorphic Hardware [50.380319968947035]
  Recent deep learning approaches have reached accuracy in such tasks, but their implementation on conventional embedded solutions is still computationally very and energy expensive. We propose a new benchmark for computing tactile pattern recognition at the edge through letters reading. We trained and compared feed-forward and recurrent spiking neural networks (SNNs) offline using back-propagation through time with surrogate gradients, then we deployed them on the Intel Loihimorphic chip for efficient inference. Our results show that the LSTM outperforms the recurrent SNN in terms of accuracy by 14%. However, the recurrent SNN on Loihi is 237 times more energy
  arXiv  Detail & Related papers  (2022-05-30T14:30:45Z)
• Training High-Performance Low-Latency Spiking Neural Networks by Differentiation on Spike Representation [70.75043144299168]
  Spiking Neural Network (SNN) is a promising energy-efficient AI model when implemented on neuromorphic hardware. It is a challenge to efficiently train SNNs due to their non-differentiability. We propose the Differentiation on Spike Representation (DSR) method, which could achieve high performance.
  arXiv  Detail & Related papers  (2022-05-01T12:44:49Z)
• Event-based Video Reconstruction via Potential-assisted Spiking Neural Network [48.88510552931186]
  Bio-inspired neural networks can potentially lead to greater computational efficiency on event-driven hardware. We propose a novel Event-based Video reconstruction framework based on a fully Spiking Neural Network (EVSNN) We find that the spiking neurons have the potential to store useful temporal information (memory) to complete such time-dependent tasks.
  arXiv  Detail & Related papers  (2022-01-25T02:05:20Z)
• Training Feedback Spiking Neural Networks by Implicit Differentiation on the Equilibrium State [66.2457134675891]
  Spiking neural networks (SNNs) are brain-inspired models that enable energy-efficient implementation on neuromorphic hardware. Most existing methods imitate the backpropagation framework and feedforward architectures for artificial neural networks. We propose a novel training method that does not rely on the exact reverse of the forward computation.
  arXiv  Detail & Related papers  (2021-09-29T07:46:54Z)
• Training Energy-Efficient Deep Spiking Neural Networks with Single-Spike Hybrid Input Encoding [5.725845886457027]
  Spiking Neural Networks (SNNs) provide higher computational efficiency in event driven neuromorphic hardware. SNNs suffer from high inference latency, resulting from inefficient input encoding and training techniques. This paper presents a training framework for low-latency energy-efficient SNNs.
  arXiv  Detail & Related papers  (2021-07-26T06:16:40Z)
• A Spiking Neural Network for Image Segmentation [3.4998703934432682]
  We convert the deep Artificial Neural Network (ANN) architecture U-Net to a Spiking Neural Network (SNN) architecture using the Nengo framework. Both rate-based and spike-based models are trained and optimized for benchmarking performance and power. The neuromorphic implementation on the Intel Loihi neuromorphic chip is over 2x more energy-efficient than conventional hardware.
  arXiv  Detail & Related papers  (2021-06-16T16:23:18Z)
• Sparse Spiking Gradient Descent [2.741266294612776]
  We present the first sparse SNN backpropagation algorithm which achieves the same or better accuracy as current state of the art methods. We show the effectiveness of our method on real datasets of varying complexity.
  arXiv  Detail & Related papers  (2021-05-18T20:00:55Z)
• Progressive Tandem Learning for Pattern Recognition with Deep Spiking Neural Networks [80.15411508088522]
  Spiking neural networks (SNNs) have shown advantages over traditional artificial neural networks (ANNs) for low latency and high computational efficiency. We propose a novel ANN-to-SNN conversion and layer-wise learning framework for rapid and efficient pattern recognition.
  arXiv  Detail & Related papers  (2020-07-02T15:38:44Z)
• You Only Spike Once: Improving Energy-Efficient Neuromorphic Inference to ANN-Level Accuracy [51.861168222799186]
  Spiking Neural Networks (SNNs) are a type of neuromorphic, or brain-inspired network. SNNs are sparse, accessing very few weights, and typically only use addition operations instead of the more power-intensive multiply-and-accumulate operations. In this work, we aim to overcome the limitations of TTFS-encoded neuromorphic systems.
  arXiv  Detail & Related papers  (2020-06-03T15:55:53Z)

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.