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Scaling Limits of Memristor-Based Routers for Asynchronous Neuromorphic Systems
Multi-core neuromorphic systems typically use on-chip routers to transmit spikes among cores.
Edge AI without Compromise: Efficient, Versatile and Accurate Neurocomputing in Resistive Random-Access Memory
Realizing today's cloud-level artificial intelligence functionalities directly on devices distributed at the edge of the internet calls for edge hardware capable of processing multiple modalities of sensory data (e. g. video, audio) at unprecedented energy-efficiency.
Improving the accuracy of neural networks in analog computing-in-memory systems by a generalized quantization method
Therefore, a suitable quantization algorithm is important when deploying a DNN into CACIM systems to obtain less accuracy loss.
Large-scale neuromorphic optoelectronic computing with a reconfigurable diffractive processing unit
Here, we propose an optoelectronic reconfigurable computing paradigm by constructing a diffractive processing unit (DPU) that can efficiently support different neural networks and achieve a high model complexity with millions of neurons.
A Training Scheme for the Uncertain Neuromorphic Computing Chips
In this work, we proposed the uncertainty adaptation training scheme (UATS) that tells the uncertainty to the neural network in the training process.
