6 citations · 6 across the 6 of their papers we have counts for
6 papers
NeuDW-CIM: a 65-nm 0.8-pJ/Sop Reconfigurable Neuromorphic Compute-in-Memory Macro with Nonlinear Dendrites and K-Winners
Junyi Yang, Yahan Yang, Shuai Dong +7
This work presents NeuDW-CIM, a highly efficient neuromorphic Compute-in-Memory (CIM) macro for Spiking Neural Networks (SNNs) implemented in 65 nm CMOS. The design introduces a cu…
In-Memory ADC-Based Nonlinear Activation Quantization for Efficient In-Memory Computing
Shuai Dong, Junyi Yang, Biyan Zhou +3
In deep networks, operations such as ReLU and hardware-driven clamping often cause activations to accumulate near the edges of the distribution, leading to biased clustering and su…
An energy-efficient spiking neural network with continuous learning for self-adaptive brain-machine interface
Zhou Biyan, Arindam Basu
The number of simultaneously recorded neurons follows an exponentially increasing trend in implantable brain-machine interfaces (iBMIs). Integrating the neural decoder in the impla…
Event-based Neural Spike Detection Using Spiking Neural Networks for Neuromorphic iBMI Systems
Chanwook Hwang, Biyan Zhou, Ye Ke +3
Implantable brain-machine interfaces (iBMIs) are evolving to record from thousands of neurons wirelessly but face challenges in data bandwidth, power consumption, and implant size.…
Architectural Exploration of Hybrid Neural Decoders for Neuromorphic Implantable BMI
Vivek Mohan, Biyan Zhou, Zhou Wang +3
This work presents an efficient decoding pipeline for neuromorphic implantable brain-machine interfaces (Neu-iBMI), leveraging sparse neural event data from an event-based neural s…
Combining SNNs with Filtering for Efficient Neural Decoding in Implantable Brain-Machine Interfaces
Biyan Zhou, Pao-Sheng Vincent Sun, Arindam Basu
While it is important to make implantable brain-machine interfaces (iBMI) wireless to increase patient comfort and safety, the trend of increased channel count in recent neural pro…