36 citations · 71 across the 15 of their papers we have counts for
3 papers · 1 filter
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…
LEGaTO: Low-Energy, Secure, and Resilient Toolset for Heterogeneous Computing
B. Salami, K. Parasyris, A. Cristal +42
The LEGaTO project leverages task-based programming models to provide a software ecosystem for Made in-Europe heterogeneous hardware composed of CPUs, GPUs, FPGAs and dataflow engi…
A Novel FPGA-Based High Throughput Accelerator For Binary Search Trees
Oyku Melikoglu, Oguz Ergin, Behzad Salami +3
This paper presents a deeply pipelined and massively parallel Binary Search Tree (BST) accelerator for Field Programmable Gate Arrays (FPGAs). Our design relies on the extremely pa…