Fig. 5: Placement of the dynamic SNNs on Speck.
From: Spike-based dynamic computing with asynchronous sensing-computing neuromorphic chip
a Speck-based neuromorphic system. b Dynamic SNN architecture deployed on Speck. We made some algorithmic adjustments based on the proposed dynamic framework, to adapt to the hardware. We only employ temporal-wise attention on the event streams to wean out which inputs can be masked. On the other hand, the spiking neuron model on Speck is Integrate and Fire (IF), i.e., LIF neuron without leaky operation. Xt,n, Ht,n, and St,n (specific definitions are given in Part “Spiking neuron models'' section) represent the spatial input, temporal input, and spike output of the spiking neuron, respectively. c Overall of Speck-based neuromorphic system with dynamic SNNs. The DVS camera only perceives and encodes the brightness change information in the visual scene (the red/green dots in the graph represent brightness increase/decrease respectively.), significantly reducing spatial redundancy compared with the traditional camera. However, the high temporal resolution of the DVS causes information redundancy in the temporal dimension. We adaptively mask some inputs using temporal attention. Since Speck is event-driven, less input means lower energy consumption. Moreover, the width of Speck kit shows is equivalent to the diameter of a coin, about 25 mm, which is convenient for edge computing scenarios. d, e On-chip real-time power based on vanilla and dynamic SNNs, respectively.
