1 citations · 1 across the 14 of their papers we have counts for
15 papers
Gradient-free Task-Conditioned Retrieval for On-Device In-Context Learning
Xinyu Luo, Hui Liu, Yihua Shao +3
On-device in-context learning (ICL) relies on pre-inference retrieval to select demonstrations for useful context before downstream model inference. This retrieval must exploit tas…
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,…
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…
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…
Efficient Test-Time Adaptation through Latent Subspace Coefficients Search
Xinyu Luo, Jie Liu, Kecheng Chen +6
Real-world deployment often exposes vision models to distribution shifts, making test-time adaptation (TTA) important for reliable prediction. Existing TTA methods can be difficult…
AnalogGenie: A Generative Engine for Automatic Discovery of Analog Circuit Topologies
Jian Gao, Weidong Cao, Junyi Yang +1
The massive and large-scale design of foundational semiconductor integrated circuits (ICs) is crucial to sustaining the advancement of many emerging and future technologies, such a…
Efficient Nonlinear Function Approximation in Analog Resistive Crossbars for Recurrent Neural Networks
Junyi Yang, Ruibin Mao, Mingrui Jiang +9
Analog In-memory Computing (IMC) has demonstrated energy-efficient and low latency implementation of convolution and fully-connected layers in deep neural networks (DNN) by using p…
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…