An Efficient Hash-based Data Structure for Dynamic Vision Sensors and its Application to Low-energy Low-memory Noise Filtering

arXiv (Cornell University)(2023)

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Events generated by the Dynamic Vision Sensor (DVS) are generally stored and processed in two-dimensional data structures whose memory complexity and energy-per-event scale proportionately with increasing sensor dimensions. In this paper, we propose a new two-dimensional data structure (BF_2) that takes advantage of the sparsity of events and enables compact storage of data using hash functions. It overcomes the saturation issue in the Bloom Filter (BF) and the memory reset issue in other hash-based arrays by using a second dimension to clear 1 out of D rows at regular intervals. A hardware-friendly, low-power, and low-memory-footprint noise filter for DVS is demonstrated using BF_2. For the tested datasets, the performance of the filter matches those of state-of-the-art filters like the BAF/STCF while consuming less than 10% and 15% of their memory and energy-per-event, respectively, for a correlation time constant Tau = 5 ms. The memory and energy advantages of the proposed filter increase with increasing sensor sizes. The proposed filter compares favourably with other hardware-friendly, event-based filters in hardware complexity, memory requirement and energy-per-event - as demonstrated through its implementation on an FPGA. The parameters of the data structure can be adjusted for trade-offs between performance and memory consumption, based on application requirements.
dynamic vision sensors,noise filtering,data structure,hash-based,low-energy,low-memory
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