  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.
