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

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 6T-SRAM in-memory computing based image denoising for event-based binary image (EBBI) frame from an NVS. We propose a non-overlap median filter (NOMF) for image denoising. An in-memory 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 > 70× energy savings and a > 3× 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.

Within-Camera Multilayer Perceptron DVS Denoising

“…We exclude denoisers that build a binary image and denoise it[21],[22] because they discard event timing and event count information.…”