7 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…
A 32-Channel 3.53-μW Per Channel Brain-Machine Interface SoC Featuring Dual-Threshold Delta-modulation, In-Memory Spike Detection and Bi-SNN Based Motor Decoding
Ye Ke, Zhengnan Fu, Pao-Sheng Vincent Sun +8
With the scaling of sensor channel counts, systems confront challenges in frontend data sensing and on-implant data processing. This work presents a 32-channel fully event-based iB…
A Reconfigurable Computing In-Memory Macro with Charge-sharing-based Weighted Accumulator
Junyi Yang, Shuai Dong, Zhengnan Fu +2
SRAM-based analog computing-in-memory demonstrates outstanding efficiency. However, it faces three critical challenges: significant ADC overhead, high latency for multi-bit inputs,…
An Event-Driven E-Skin System with Dynamic Binary Scanning and real time SNN Classification
Gaishan Li, Zhengnan Fu, Anubhab Tripathi +2
This paper presents a novel hardware system for high-speed, event-sparse sampling-based electronic skin (e-skin)that integrates sensing and neuromorphic computing. The system is bu…
STEMNIST: Spiking Tactile Extended MNIST Neuromorphic Dataset
Anubhab Tripathi, Li Gaishan, Zhengnan Fu +3
Tactile sensing is essential for robotic manipulation, prosthetics and assistive technologies, yet neuromorphic tactile datasets remain limited compared to their visual counterpart…
1024-Channel 0.8V 23.9-nW/Channel Event-based Compute In-memory Neural Spike Detector
Ye Ke, Zhengnan Fu, Junyi Yang +2
The increasing data rate has become a major issue confronting next-generation intracortical brain-machine interfaces (iBMIs). The scaling number of recording sites requires complex…