19 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,…
SNNF: An SNN-based Near-Sensor Noise Filter for Dynamic Vision Sensors
Yahan Yang, Pradeep Kumar Gopalakrishnan, Chang Chip Hong +1
Dynamic Vision Sensors (DVS) exhibit exceptional dynamic range and low power consumption, making them ideal for edge applications in the Internet of Video Things (IoVT). However, t…
SRAM-Based Compute-in-Memory Accelerator for Linear-decay Spiking Neural Networks
Hongyang Shang, Shuai Dong, Yahan Yang +3
Spiking Neural Networks (SNNs) have emerged as a biologically inspired alternative to conventional deep networks, offering event-driven and energy-efficient computation. However, t…
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 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…
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.…
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…
Topkima-Former: Low-energy, Low-Latency Inference for Transformers using top-k In-memory ADC
Shuai Dong, Junyi Yang, Xiaoqi Peng +5
Transformer model has gained prominence as a popular deep neural network architecture for neural language processing (NLP) and computer vision (CV) applications. However, the exten…
A Low-Power Spike Detector Using In-Memory Computing for Event-based Neural Frontend
Ye Ke, Arindam Basu
With the sensor scaling of next-generation Brain-Machine Interface (BMI) systems, the massive A/D conversion and analog multiplexing at the neural frontend poses a challenge in ter…
Hybrid Event-Frame Neural Spike Detector for Neuromorphic Implantable BMI
Vivek Mohan, Wee Peng Tay, Arindam Basu
This work introduces two novel neural spike detection schemes intended for use in next-generation neuromorphic brain-machine interfaces (iBMIs). The first, an Event-based Spike Det…
Towards Neuromorphic Compression based Neural Sensing for Next-Generation Wireless Implantable Brain Machine Interface
Vivek Mohan, Wee Peng Tay, Arindam Basu
This work introduces a neuromorphic compression based neural sensing architecture with address-event representation inspired readout protocol for massively parallel, next-gen wirel…