A 51.3 TOPS/W, 134.4 GOPS In-memory Binary Image Filtering in 65nm CMOS

TL;DR

This paper introduces an in-memory computing architecture using 6T-SRAM for energy-efficient image denoising in neuromorphic vision sensors, achieving high throughput and energy efficiency while maintaining accuracy.

Contribution

It presents a novel in-memory filtering framework with a non-overlap median filter leveraging 6T-SRAM's read disturb phenomenon, demonstrating significant energy and speed improvements.

Findings

Achieves >70x energy savings over digital implementations.

Provides >3x faster processing times.

Attains 51.3 TOPS/W energy efficiency, comparable to state-of-the-art in-memory processors.

Abstract

Neuromorphic vision sensors (NVS) can enable energy savings due to their event-driven that exploits the temporal redundancy in video streams from a stationary camera. However, noise-driven events lead to the false triggering of the object recognition processor. Image denoise operations require memoryintensive processing leading to a bottleneck in energy and latency. In this paper, we present in-memory filtering (IMF), a 6TSRAM in-memory computing based image denoising for eventbased binary image (EBBI) frame from an NVS. We propose a non-overlap median filter (NOMF) for image denoising. An inmemory computing framework enables hardware implementation of NOMF leveraging the inherent read disturb phenomenon of 6T-SRAM. To demonstrate the energy-saving and effectiveness of the algorithm, we fabricated the proposed architecture in a 65nm CMOS process. As compared to fully digital implementation, IMF enables > 70x energy savings and a > 3x improvement of processing time when tested with the video recordings from a DAVIS sensor and achieves a peak throughput of 134.4 GOPS. Furthermore, the peak energy efficiencies of the NOMF is 51.3 TOPS/W, comparable with state of the art inmemory processors. We also show that the accuracy of the images obtained by NOMF provide comparable accuracy in tracking and classification applications when compared with images obtained by conventional median filtering.