Search Results - "Computer systems organization Embedded and cyber-physical systems Embedded systems"

CirCNN: accelerating and compressing deep neural networks using block-circulant weight matrices by Ding, Caiwen, Liao, Siyu, Wang, Yanzhi, Li, Zhe, Liu, Ning, Zhuo, Youwei, Wang, Chao, Qian, Xuehai, Bai, Yu, Yuan, Geng, Ma, Xiaolong, Zhang, Yipeng, Tang, Jian, Qiu, Qinru, Lin, Xue, Yuan, Bo

“…Large-scale deep neural networks (DNNs) are both compute and memory intensive. As the size of DNNs continues to grow, it is critical to improve the energy…”
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Reinforcement Learning-Assisted Management for Convertible SSDs by Wei, Qian, Li, Yi, Jia, Zhiping, Zhao, Mengying, Shen, Zhaoyan, Li, Bingzhe

“…Convertible SSDs, which allow flash cells to convert between different types of flash cells (e.g., SLC/MLC/TLC/QLC), are designed for achieving both high…”
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Watermarking Deep Neural Networks for Embedded Systems by Guo, Jia, Potkonjak, Miodrag

“…Deep neural networks (DNNs) have become an important tool for bringing intelligence to mobile and embedded devices. The increasingly wide deployment, sharing…”
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Remote Inter-Chip Power Analysis Side-Channel Attacks at Board-Level by Schellenberg, Falk, Gnad, Dennis R.E., Moradi, Amir, Tahoori, Mehdi B.

“…The current practice in board-level integration is to incorporate chips and components from numerous vendors. A fully trusted supply chain for all used…”
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Control Variate Approximation for DNN Accelerators by Zervakis, Georgios, Spantidi, Ourania, Anagnostopoulos, Iraklis, Amrouch, Hussam, Henkel, Jorg

“…In this work, we introduce a control variate approximation technique for low error approximate Deep Neural Network (DNN) accelerators. The control variate…”
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DRAMScope: Uncovering DRAM Microarchitecture and Characteristics by Issuing Memory Commands by Nam, Hwayong, Baek, Seungmin, Wi, Minbok, Kim, Michael Jaemin, Park, Jaehyun, Song, Chihun, Kim, Nam Sung, Ahn, Jung Ho

“…The demand for precise information on DRAM microarchitectures and error characteristics has surged, driven by the need to explore processing in memory, enhance…”
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OmniBoost: Boosting Throughput of Heterogeneous Embedded Devices under Multi-DNN Workload by Karatzas, Andreas, Anagnostopoulos, Iraklis

“…Modern Deep Neural Networks (DNNs) exhibit profound efficiency and accuracy properties. This has introduced application workloads that comprise of multiple DNN…”
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DyREM: Dynamically Mitigating Quantum Readout Error with Embedded Accelerator by Zhou, Kaiwen, Lu, Liqiang, Zhang, Hanyu, Xiang, Debin, Tao, Chenning, Zhao, Xinkui, Zheng, Size, Yin, Jianwei

“…Quantum readout error is the most significant source of error, substantially reducing the measurement fidelity. Tensor-product-based readout error mitigation…”
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Analyzing and Improving Fault Tolerance of Learning-Based Navigation Systems by Wan, Zishen, Anwar, Aqeel, Hsiao, Yu-Shun, Jia, Tianyu, Reddi, Vijay Janapa, Raychowdhury, Arijit

“…Learning-based navigation systems are widely used in autonomous applications, such as robotics, unmanned vehicles and drones. Specialized hardware accelerators…”
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TIE: Energy-efficient Tensor Train-based Inference Engine for Deep Neural Network by Deng, Chunhua, Sun, Fangxuan, Qian, Xuehai, Lin, Jun, Wang, Zhongfeng, Yuan, Bo

“…In the era of artificial intelligence (AI), deep neural networks (DNNs) have emerged as the most important and powerful AI technique. However, large DNN models…”
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Guarder: A Stable and Lightweight Reconfigurable RRAM-based PIM Accelerator for DNN IP Protection by Lin, Ning, Li, Yi, Li, Jiankun, Yang, Jichang, He, Yangu, Luo, Yukui, Shang, Dashan, Chen, Xiaoming, Qi, Xiaojuan, Wang, Zhongrui

“…Deploying deep neural networks (DNNs) on conventional digital edge devices faces significant challenges due to high energy consumption. A promising solution is…”
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LA-MTL: Latency-Aware Automated Multi-Task Learning by Sampath, Shambhavi Balamuthu, Sawani, Sami, Thoma, Moritz, Frickenstein, Lukas, Mori, Pierpaolo, Fasfous, Nael, Vemparala, Manoj Rohit, Frickenstein, Alexander, Schlichtmann, Ulf, Passerone, Claudio, Stechele, Walter

“…Multi-Task Learning (MTL) aims to unify a variety of tasks into a single network for improved training and inference efficiency. This is particularly…”
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Exploiting Power Side-Channel Vulnerabilities in XGBoost Accelerator by Xiao, Yimeng, Gajjar, Archit, Aysu, Aydin, Franzon, Paul

“…XGBoost (eXtreme Gradient Boosting), a widelyused decision tree algorithm, plays a crucial role in applications such as ransomware and fraud detection. While…”
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POLARIS: Explainable Artificial Intelligence for Mitigating Power Side-Channel Leakage by Mahfuz, Tanzim, Paria, Sudipta, Suha, Tasneem, Bhunia, Swarup, Chakraborty, Prabuddha

“…Microelectronic systems are widely used in many sensitive applications (e.g., manufacturing, energy, defense). These systems increasingly handle sensitive data…”
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Late Breaking Results: Novel Design of MTJ-Based Unified LIF Spiking Neuron and PUF by Nasab, Milad Tanavardi, Yang, Wu, Thapliyal, Himanshu

“…Due to the higher energy and hardware efficiency of spiking neural networks (SNNs) compared to deep neural networks, they have attracted a lot of attention…”
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SSDTrain: An Activation Offloading Framework to SSDs for Faster Large Language Model Training by Wu, Kun, Park, Jeongmin Brian, Zhang, Xiaofan, Hidayetoglu, Mert, Mailthody, Vikram Sharma, Huang, Sitao, Lumetta, Steve, Hwu, Wen-Mei

“…The growth rate of the GPU memory capacity has not been able to keep up with that of the size of large language models (LLMs), hindering the model training…”
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GLiTCH: GLiTCH induced Transitions for Secure Crypto-Hardware by Menon, C. Rohin, Balasubramanian, Jayanth, Akshay Kumar, E, Valiveti, Annapurna, Rebeiro, Chester, Viraraghavan, Janakiraman

“…Conventionally, glitch reduction is well-studied in digital design to improve power, efficiency, and security. In contrast, this paper combines the addition…”
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Security of Approximate Neural Networks against Power Side-channel Attacks by Japa, Aditya, Miskelly, Jack, O'Neill, Maire, Gu, Chongyan

“…Emerging low-energy computing technologies, in particular approximate computing, are becoming increasingly relevant in key applications. A significant use case…”
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Hypnos: Memory Efficient Homomorphic Processing Unit by Wang, Haoxuan, Yang, Yinghao, Lu, Hang, Li, Xiaowei

“…Fully Homomorphic Encryption (FHE) introduces a novel paradigm in privacy-preserving computation, extending its applicability to various scenarios. However,…”
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Learning-Aided Safe Controller Synthesis with Formal Guarantees via Vector Barrier Certificates by Zeng, Xia, Ren, Mengxin, Liu, Zhiming, Yang, Zhengfeng

“…The design of controllers for safety-critical systems is an important research issue. Especially, the generation of controllers with formal safety guarantees…”
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