36 citations · 61 across the 7 of their papers we have counts for
11 papers
On the Impact of Device-Level Techniques on Energy-Efficiency of Neural Network Accelerators
Seyed Morteza Nabavinejad, Behzad Salami
Energy-efficiency is a key concern for neural network applications. To alleviate this issue, hardware acceleration using FPGAs or GPUs can provide better energy-efficiency than gen…
Understanding Power Consumption and Reliability of High-Bandwidth Memory with Voltage Underscaling
Seyed Saber Nabavi Larimi, Behzad Salami, Osman S. Unsal +3
Modern computing devices employ High-Bandwidth Memory (HBM) to meet their memory bandwidth requirements. An HBM-enabled device consists of multiple DRAM layers stacked on top of on…
Exceeding Conservative Limits: A Consolidated Analysis on Modern Hardware Margins
George Papadimitriou, Athanasios Chatzidimitriou, Dimitris Gizopoulos +5
Modern large-scale computing systems (data centers, supercomputers, cloud and edge setups and high-end cyber-physical systems) employ heterogeneous architectures that consist of mu…
Power and Accuracy of Multi-Layer Perceptrons (MLPs) under Reduced-voltage FPGA BRAMs Operation
Behzad Salami, Osman Unsal, Adrian Cristal
In this paper, we exploit the aggressive supply voltage underscaling technique in Block RAMs (BRAMs) of Field Programmable Gate Arrays (FPGAs) to improve the energy efficiency of M…
An Experimental Study of Reduced-Voltage Operation in Modern FPGAs for Neural Network Acceleration
Behzad Salami, Erhan Baturay Onural, Ismail Emir Yuksel +6
We empirically evaluate an undervolting technique, i.e., underscaling the circuit supply voltage below the nominal level, to improve the power-efficiency of Convolutional Neural Ne…
On the Resilience of Deep Learning for Reduced-voltage FPGAs
Kamyar Givaki, Behzad Salami, Reza Hojabr +6
Deep Neural Networks (DNNs) are inherently computation-intensive and also power-hungry. Hardware accelerators such as Field Programmable Gate Arrays (FPGAs) are a promising solutio…