Spiking neural network integrated circuits: A review of trends and future directions
The rapid growth of deep learning, spurred by its successes in various fields ranging from
face recognition [1] to game playing [2], has also triggered a growing interest in the design of …
face recognition [1] to game playing [2], has also triggered a growing interest in the design of …
Bottom-up and top-down approaches for the design of neuromorphic processing systems: tradeoffs and synergies between natural and artificial intelligence
While Moore's law has driven exponential computing power expectations, its nearing end
calls for new avenues for improving the overall system performance. One of these avenues …
calls for new avenues for improving the overall system performance. One of these avenues …
Surrogate gradients for analog neuromorphic computing
B Cramer, S Billaudelle, S Kanya… - Proceedings of the …, 2022 - National Acad Sciences
To rapidly process temporal information at a low metabolic cost, biological neurons integrate
inputs as an analog sum, but communicate with spikes, binary events in time. Analog …
inputs as an analog sum, but communicate with spikes, binary events in time. Analog …
Bottom-up and top-down neural processing systems design: Neuromorphic intelligence as the convergence of natural and artificial intelligence
While Moore's law has driven exponential computing power expectations, its nearing end
calls for new avenues for improving the overall system performance. One of these avenues …
calls for new avenues for improving the overall system performance. One of these avenues …
Learning without feedback: Fixed random learning signals allow for feedforward training of deep neural networks
While the backpropagation of error algorithm enables deep neural network training, it
implies (i) bidirectional synaptic weight transport and (ii) update locking until the forward and …
implies (i) bidirectional synaptic weight transport and (ii) update locking until the forward and …
Improving the accuracy of spiking neural networks for radar gesture recognition through preprocessing
Event-based neural networks are currently being explored as efficient solutions for
performing AI tasks at the extreme edge. To fully exploit their potential, event-based neural …
performing AI tasks at the extreme edge. To fully exploit their potential, event-based neural …
ANP-I: A 28-nm 1.5-pJ/SOP Asynchronous Spiking Neural Network Processor Enabling Sub-0.1-J/Sample On-Chip Learning for Edge-AI Applications
Reducing learning energy consumption is critical to edge-artificial intelligence (AI)
processors with on-chip learning since on-chip learning energy dominates energy …
processors with on-chip learning since on-chip learning energy dominates energy …
Neurobench: Advancing neuromorphic computing through collaborative, fair and representative benchmarking
The field of neuromorphic computing holds great promise in terms of advancing computing
efficiency and capabilities by following brain-inspired principles. However, the rich diversity …
efficiency and capabilities by following brain-inspired principles. However, the rich diversity …
Neuromorphic control for optic-flow-based landing of MAVs using the Loihi processor
Neuromorphic processors like Loihi offer a promising alternative to conventional computing
modules for endowing constrained systems like micro air vehicles (MAVs) with robust …
modules for endowing constrained systems like micro air vehicles (MAVs) with robust …
The backpropagation algorithm implemented on spiking neuromorphic hardware
The capabilities of natural neural systems have inspired both new generations of machine
learning algorithms as well as neuromorphic, very large-scale integrated circuits capable of …
learning algorithms as well as neuromorphic, very large-scale integrated circuits capable of …