  In-camera event denoising reduces the data rate of event cameras by filtering
out noise at the source. A lightweight multilayer perceptron denoising filter
(MLPF) provides state-of-the-art low-cost denoising accuracy. It processes a
small neighborhood of pixels from the timestamp image around each event to
discriminate signal and noise events. This paper proposes two digital logic
implementations of the MLPF denoiser and quantifies their resource cost, power,
and latency. The hardware MLPF quantizes the weights and hidden unit
activations to 4 bits and has about 1k weights with about 40% sparsity. The
Area-Under-Curve Receiver Operating Characteristic accuracy is nearly
indistinguishable from that of the floating point network. The FPGA MLPF
processes each event in 10 clock cycles. In FPGA, it uses 3.5k flip flops and
11.5k LUTs. Our ASIC implementation in 65nm digital technology for a 346x260
pixel camera occupies an area of 4.3mm^2 and consumes 4nJ of energy per event
at event rates up to 25MHz. The MLPF can be easily integrated into an event
camera using an FPGA or as an ASIC directly on the camera chip or in the same
package. This denoising could dramatically reduce the energy consumed by the
communication and host processor and open new areas of always-on event camera
application under scavenged and battery power. Code:
https://github.com/SensorsINI/dnd_hls
