Zobrazeno 1 - 10
of 146
pro vyhledávání: '"Ghavami, Behnam"'
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
YOLO is a deep neural network (DNN) model presented for robust real-time object detection following the one-stage inference approach. It outperforms other real-time object detectors in terms of speed and accuracy by a wide margin. Nevertheless, since
Externí odkaz:
http://arxiv.org/abs/2407.04943
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
The problem of chemotherapy treatment optimization can be defined in order to minimize the size of the tumor without endangering the patient's health; therefore, chemotherapy requires to achieve a number of objectives, simultaneously. For this reason
Externí odkaz:
http://arxiv.org/abs/2303.10535
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
Autor:
Farahmand, Ebrahim, Mahani, Ali, Ghavami, Behnam, Hanif, Muhammad Abdullah, Shafique, Muhammad
Approximate computing (AC) has become a prominent solution to improve the performance, area, and power/energy efficiency of a digital design at the cost of output accuracy. We propose a novel scalable approximate multiplier that utilizes a lookup tab
Externí odkaz:
http://arxiv.org/abs/2303.02495
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