21 citations · 34 across the 10 of their papers we have counts for
11 papers
A Secure dToF LiDAR SoC with Dual-Domain Fingerprinting and Event-Driven AFE Circuit Achieving Sensor-Level Attack Resilience
Risa Nonaka, Ryoya Matsuno, Shota Nagai +10
Recent studies have shown that most commercial direct time-of-flight (dToF) LiDARs can be spoofed by injecting high-frequency laser pulses into the receiver, which can erase pedest…
Lean-SAM2: Target-Anchored Memory and Encoder Acceleration for SAM2
Xudong Ouyang, Wenlun Zhang, Yimin Xu +2
The Segment Anything Model 2 (SAM2) has advanced temporal promptable segmentation, yet its deployment remains hindered by heavy memory cross-attention overhead and redundant full-f…
Detect in Any Scene: An Agentic Framework for Object Detection with Experience-Aware Reasoning
Wenlun Zhang, Jun Yin, Kentaro Yoshioka
Object detection in real-world scenarios remains challenging due to diverse image degradations and heterogeneous object distributions, which significantly hinder the generalization…
Neural Reconstruction of LiDAR Point Clouds under Jamming Attacks via Full-Waveform Representation and Simultaneous Laser Sensing
Ryo Yoshida, Takami Sato, Wenlun Zhang +7
LiDAR sensors are critical for autonomous driving perception, yet remain vulnerable to spoofing attacks. Jamming attacks inject high-frequency laser pulses that completely blind Li…
D4C: Data-Free Quantization for Contrastive Language-Image Pre-training Models
Wenlun Zhang, Yunshan Zhong, Zihao Ding +2
Data-Free Quantization (DFQ) offers a practical solution for model compression without requiring access to real data, making it particularly attractive in privacy-sensitive scenari…
BitROM: Weight Reload-Free CiROM Architecture Towards Billion-Parameter 1.58-bit LLM Inference
Wenlun Zhang, Xinyu Li, Shimpei Ando +1
Compute-in-Read-Only-Memory (CiROM) accelerators offer outstanding energy efficiency for CNNs by eliminating runtime weight updates. However, their scalability to Large Language Mo…
AHCQ-SAM: Toward Accurate and Hardware-Compatible Post-Training Segment Anything Model Quantization
Wenlun Zhang, Yunshan Zhong, Weiqi Yan +3
The Segment Anything Model (SAM) has revolutionized image and video segmentation with its powerful zero-shot capabilities. However, its massive parameter scale and high computation…
LiSA: Leveraging Link Recommender to Attack Graph Neural Networks via Subgraph Injection
Wenlun Zhang, Enyan Dai, Kentaro Yoshioka
Graph Neural Networks (GNNs) have demonstrated remarkable proficiency in modeling data with graph structures, yet recent research reveals their susceptibility to adversarial attack…
A Review of SRAM-based Compute-in-Memory Circuits
Kentaro Yoshioka, Shimpei Ando, Satomi Miyagi +2
This paper presents a tutorial and review of SRAM-based Compute-in-Memory (CIM) circuits, with a focus on both Digital CIM (DCIM) and Analog CIM (ACIM) implementations. We explore…
ASiM: Modeling and Analyzing Inference Accuracy of SRAM-Based Analog CiM Circuits
Wenlun Zhang, Shimpei Ando, Yung-Chin Chen +1
SRAM-based Analog Compute-in-Memory (ACiM) demonstrates promising energy efficiency for deep neural network (DNN) processing. Nevertheless, efforts to optimize efficiency frequentl…
PACiM: A Sparsity-Centric Hybrid Compute-in-Memory Architecture via Probabilistic Approximation
Wenlun Zhang, Shimpei Ando, Yung-Chin Chen +3
Approximate computing emerges as a promising approach to enhance the efficiency of compute-in-memory (CiM) systems in deep neural network processing. However, traditional approxima…