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

Neuromorphic vision sensors (NVSs) 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 memory-intensive processing leading to a bottleneck in energy and latency. In this article, we present in-memory filtering (IMF), a 6T-SRAM in-memory computing (IMC)-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 IMC 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 65-nm CMOS process. Compared to fully digital implementation, IMF enables >70 <tex-math notation="LaTeX">× </tex-math> energy savings and a >3 <tex-math notation="LaTeX">× </tex-math> 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 are 51.3 TOPS/W, comparable with state-of-the-art in-memory processors. We also show that the accuracy of the images obtained by NOMF provides comparable accuracy in tracking and classification applications compared with images obtained by conventional median filtering.