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of 85
pro vyhledávání: '"Shannon, Lesley"'
Low-precision weights and activations in deep neural networks (DNNs) outperform their full-precision counterparts in terms of hardware efficiency. When implemented with low-precision operations, specifically in the extreme case where network paramete
Externí odkaz:
http://arxiv.org/abs/2407.04964
The imperative to deploy Deep Neural Network (DNN) models on resource-constrained edge devices, spurred by privacy concerns, has become increasingly apparent. To facilitate the transition from cloud to edge computing, this paper introduces a techniqu
Externí odkaz:
http://arxiv.org/abs/2404.02947
Many aerospace and automotive applications use FPGAs in their designs due to their low power and reconfigurability requirements. Meanwhile, such applications also pose a high standard on system reliability, which makes the early-stage reliability ana
Externí odkaz:
http://arxiv.org/abs/2303.12269
Autor:
Ghavami, Behnam, Shannon, Lesley
This paper presents an overview of the integration of deep machine learning (DL) in FPGA CAD design flow, focusing on high-level and logic synthesis, placement, and routing. Our analysis identifies key research areas that require more attention in FP
Externí odkaz:
http://arxiv.org/abs/2303.10508
Deep neural networks (DNNs) are increasingly being deployed in safety-critical systems such as personal healthcare devices and self-driving cars. In such DNN-based systems, error resilience is a top priority since faults in DNN inference could lead t
Externí odkaz:
http://arxiv.org/abs/2112.13544
Recently, deep neural networks (DNNs) have been deployed in safety-critical systems such as autonomous vehicles and medical devices. Shortly after that, the vulnerability of DNNs were revealed by stealthy adversarial examples where crafted inputs --
Externí odkaz:
http://arxiv.org/abs/2112.13162
Autor:
Saremi, Kiarash, Pedram, Hossein, Ghavami, Behnam, Raji, Mohsen, Fang, Zhenman, Shannon, Lesley
Nowadays nanoscale combinational circuits are facing significant reliability challenges including soft errors and process variations. This paper presents novel process variation-aware placement strategies that include two algorithms to increase the r
Externí odkaz:
http://arxiv.org/abs/2112.04136
Adversarial bit-flip attack (BFA) on Neural Network weights can result in catastrophic accuracy degradation by flipping a very small number of bits. A major drawback of prior bit flip attack techniques is their reliance on test data. This is frequent
Externí odkaz:
http://arxiv.org/abs/2112.03477
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When designing modern embedded computing systems, most software programmers choose to use multicore processors, possibly in combination with general-purpose graphics processing units (GPGPUs) and/or hardware accelerators. They also often use an embed
Externí odkaz:
http://arxiv.org/abs/1508.07126