7 papers
3D Stack In-Sensor-Computing (3DS-ISC): Accelerating Time-Surface Construction for Neuromorphic Event Cameras
Hongyang Shang, Shuai Dong, Ye Ke +1
This work proposes a 3D Stack In-Sensor-Computing (3DS-ISC) architecture for efficient event-based vision processing. A real-time normalization method using an exponential decay fu…
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…
A 33.6-136.2 TOPS/W Nonlinear Analog Computing-In-Memory Macro for Multi-bit LSTM Accelerator in 65 nm CMOS
Junyi Yang, Xinyu Luo, Ye Ke +7
The energy efficiency of analog computing-in-memory (ACIM) accelerator for recurrent neural networks, particularly long short-term memory (LSTM) network, is limited by the high pro…
Near-Memory Architecture for Threshold-Ordinal Surface-Based Corner Detection of Event Cameras
Hongyang Shang, An Guo, Shuai Dong +3
Event-based Cameras (EBCs) are widely utilized in surveillance and autonomous driving applications due to their high speed and low power consumption. Corners are essential low-leve…
CADC: Crossbar-Aware Dendritic Convolution for Efficient In-memory Computing
Shuai Dong, Junyi Yang, Ye Ke +2
Convolutional neural networks (CNNs) are computationally intensive and often accelerated using crossbar-based in-memory computing (IMC) architectures. However, large convolutional…
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.…