Silicon Feeds
Spiking neural networks (SNNs) are well suited to event-driven edge intelligence, but the arithmetic complexity, memory footprint, and power consumption associated with conventional floating-point implementations can limit their deployment on resource-constrained hardware. This paper presents a precision-scalable and…
Spiking neural networks (SNNs) expose sparse event activity, but commodity microcontrollers increasingly pair general-purpose processors with dense matrix-vector accelerators whose suitability for sparse inference is unclear. We investigate whether the on-chip Matrix Vector Processor (MVP) of the EFR32MG24 can…
Decoding visual stimuli from the spiking activity of retinal ganglion cells (RGCs) informs both our understanding of the retinal code and the design of visual prostheses. Natural vision is dynamic, yet state-of-the-art decoders mostly reconstruct single frames with artificial neural networks that process dense,…