mlsanityData to 14 Oct 2026

In-Sensor & Neuromorphic Computing are all you need for Energy Efficient Computer Vision

arXiv 2212.10881 ↗First posted 21 Dec 2022cs.CVcited by 6

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Abstract (arXiv)

Due to the high activation sparsity and use of accumulates (AC) instead of expensive multiply-and-accumulates (MAC), neuromorphic spiking neural networks (SNNs) have emerged as a promising low-power alternative to traditional DNNs for several computer vision (CV) applications. However, most existing SNNs require multiple time steps for acceptable inference accuracy, hindering real-time deployment and increasing spiking activity and, consequently, energy consumption. Recent works proposed direct encoding that directly feeds the analog pixel values in the first layer of the SNN in order to significantly reduce the number of time steps. Although the overhead for the first layer MACs with direct encoding is negligible for deep SNNs and the CV processing is efficient using SNNs, the data transfer between the image sensors and the downstream processing costs significant bandwidth and may dominate the total energy. To mitigate this concern, we propose an in-sensor computing hardware-software co-design framework for SNNs targeting image recognition tasks. Our approach reduces the bandwidth between sensing and processing by 12-96x and the resulting total energy by 2.32x compared to traditional CV processing, with a 3.8% reduction in accuracy on ImageNet.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 21 of its 36 references that are in the map. First posted on arXiv 21 Dec 2022.

Builds on

21 of 36 references.

References · 21

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,364
  3. 2016Paying More Attention to Attention: Improving the Performance of Convolutional Neural Networks via Attention Transfercited by 1,077
  4. 2013Do Deep Nets Really Need to be Deep?cited by 474
  5. 2018Going Deeper in Spiking Neural Networks: VGG and Residual Architecturescited by 245
  6. 2016Training Deep Spiking Neural Networks using Backpropagationcited by 205
  7. 2020Revisiting Batch Normalization for Training Low-latency Deep Spiking Neural Networks from Scratchcited by 80
  8. 2020DIET-SNN: Direct Input Encoding With Leakage and Threshold Optimization in Deep Spiking Neural Networkscited by 71
  9. 2019Temporal Coding in Spiking Neural Networks with Alpha Synaptic Function: Learning with Backpropagationcited by 45
  10. 2016Conversion of Artificial Recurrent Neural Networks to Spiking Neural Networks for Low-power Neuromorphic Hardwarecited by 40
  11. 2016STDP-based spiking deep convolutional neural networks for object recognitioncited by 24
  12. 2022P2M: A Processing-in-Pixel-in-Memory Paradigm for Resource-Constrained TinyML Applicationscited by 16
9 more
  1. 2020Fully Embedding Fast Convolutional Networks on Pixel Processor Arrayscited by 13
  2. 2021Training Energy-Efficient Deep Spiking Neural Networks with Single-Spike Hybrid Input Encodingcited by 10
  3. 2021Can Deep Neural Networks be Converted to Ultra Low-Latency Spiking Neural Networks?cited by 9
  4. 2021Towards Low-Latency Energy-Efficient Deep SNNs via Attention-Guided Compressioncited by 7
  5. 2022P2M-DeTrack: Processing-in-Pixel-in-Memory for Energy-efficient and Real-Time Multi-Object Detection and Trackingcited by 6
  6. 2020Fundamental Limits on Energy-Delay-Accuracy of In-memory Architectures in Inference Applicationscited by 4
  7. 2022Toward Efficient Hyperspectral Image Processing inside Camera Pixelscited by 4
  8. 2022Hoyer regularizer is all you need for ultra low-latency spiking neural networkscited by 4
  9. 2022Towards Energy-Efficient, Low-Latency and Accurate Spiking LSTMscited by 3

Built on by

Cited by year

Citing papers per year: 2023: 3; 2024: 3.

6 papers.

  1. 2024MaskVD: Region Masking for Efficient Video Object Detectioncited by 5
  2. 2023Recent Advances in Scalable Energy-Efficient and Trustworthy Spiking Neural networks: from Algorithms to Technologycited by 3
  3. 2023Let's Roll: Synthetic Dataset Analysis for Pedestrian Detection Across Different Shutter Typescited by 1
  4. 2024Energy-Efficient & Real-Time Computer Vision with Intelligent Skipping via Reconfigurable CMOS Image Sensorscited by 1
  5. 2023When Bio-Inspired Computing meets Deep Learning: Low-Latency, Accurate, & Energy-Efficient Spiking Neural Networks from Artificial Neural Networkscited by 0
  6. 2024ReDistill: Residual Encoded Distillation for Peak Memory Reduction of CNNscited by 0