Search Results for author: Behzad Salami

Found 7 papers, 1 papers with code

NEON: Enabling Efficient Support for Nonlinear Operations in Resistive RAM-based Neural Network Accelerators

no code implementations10 Nov 2022 Aditya Manglik, Minesh Patel, Haiyu Mao, Behzad Salami, Jisung Park, Lois Orosa, Onur Mutlu

Resistive Random-Access Memory (RRAM) is well-suited to accelerate neural network (NN) workloads as RRAM-based Processing-in-Memory (PIM) architectures natively support highly-parallel multiply-accumulate (MAC) operations that form the backbone of most NN workloads.

Compiler Optimization

Power and Accuracy of Multi-Layer Perceptrons (MLPs) under Reduced-voltage FPGA BRAMs Operation

no code implementations10 May 2020 Behzad Salami, Osman Unsal, Adrian Cristal

Based on the characterization of these undervolting faults, we propose fault mitigation techniques that can effectively improve the resilience behavior of such accelerator.

On the Resilience of Deep Learning for Reduced-voltage FPGAs

no code implementations26 Dec 2019 Kamyar Givaki, Behzad Salami, Reza Hojabr, S. M. Reza Tayaranian, Ahmad Khonsari, Dara Rahmati, Saeid Gorgin, Adrian Cristal, Osman S. Unsal

This paper experimentally evaluates the resilience of the training phase of DNNs in the presence of voltage underscaling related faults of FPGAs, especially in on-chip memories.

Hardware Versus Software Fault Injection of Modern Undervolted SRAMs

2 code implementations30 Nov 2019 Muhammet Abdullah Soyturk, Konstantinos Parasyris, Behzad Salami, Osman Unsal, Gulay Yalcin, Leonardo Bautista Gomez

During our study we corrupt the L1-Dcache of the simulated system and we monitor the behavior of the two types of fault maps on the resiliency of six benchmarks.

Performance Hardware Architecture

Evaluating Built-in ECC of FPGA on-chip Memories for the Mitigation of Undervolting Faults

no code implementations29 Mar 2019 Behzad Salami, Osman S. Unsal, Adrian Cristal Kestelman

In consequence, we achieve 40% of the BRAM power saving through undervolting below the minimum safe voltage level, with a negligible NN accuracy loss, thanks to the substantial fault coverage by the built-in ECC.

On the Resilience of RTL NN Accelerators: Fault Characterization and Mitigation

no code implementations14 Jun 2018 Behzad Salami, Osman Unsal, Adrian Cristal

Machine Learning (ML) is making a strong resurgence in tune with the massive generation of unstructured data which in turn requires massive computational resources.

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