Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
97 tokens/sec
GPT-4o
53 tokens/sec
Gemini 2.5 Pro Pro
43 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
47 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

In-Sensor & Neuromorphic Computing are all you need for Energy Efficient Computer Vision (2212.10881v1)

Published 21 Dec 2022 in cs.CV

Abstract: 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.

User Edit Pencil Streamline Icon: https://streamlinehq.com
Authors (9)
  1. Gourav Datta (34 papers)
  2. Zeyu Liu (54 papers)
  3. Md Abdullah-Al Kaiser (17 papers)
  4. Souvik Kundu (76 papers)
  5. Joe Mathai (8 papers)
  6. Zihan Yin (16 papers)
  7. Ajey P. Jacob (13 papers)
  8. Akhilesh R. Jaiswal (17 papers)
  9. Peter A. Beerel (66 papers)
Citations (11)

Summary

We haven't generated a summary for this paper yet.