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Rapid reaction to a specific event is a critical feature for an embedded computer vision system to ensure reliable and secure interaction with the environment in resource-limited real-time applications. This requires high-level scene understanding with ultra-fast processing capabilities and the ability to operate at extremely low power. Existing vision systems, which rely on traditional computation techniques, including deep learning-based approaches, are limited by the compute capabilities due to large power dissipation and slow off-chip memory access. These challenges are evident in environments with constrained power, bandwidth and hardware resources, such as in the applications of drones and robot navigation in expansive areas.
A new NEuromorphic Vision System (NEVIS) is proposed to address the limitations of existing computer vision systems for many resource-limited real-time applications. NEVIS mimics the efficiency of the human visual system by encoding visual signals into spikes, which are processed by neurons with synaptic connections. The potential of NEVIS is explored through an FPGA-based accelerator implementation on a Xilinx Kria board that achieved 40× speed up compared to a Raspberry Pi 4B CPU. This work informs the future potential of NEVIS in embedded computer vision system development.
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