AMR-MUL: An Approximate Maximally Redundant Signed Digit Multiplier

Autor: Amanollahi, Saba, Kamal, Mehdi, Ali-Afzali-Kusha, Pedram, Massoud

In this paper, we present an energy-efficient, yet high-speed approximate maximally redundant signed digit (MRSD) multiplier (called AMR-MUL) based on a parallel structure. For the reduction stage, we suggest several approximate Full-Adder (FA) reduc

Externí odkaz: http://arxiv.org/abs/2208.13850

Accuracy Configurable Adders with Negligible Delay Overhead in Exact Operating Mode

Autor: Farhad Ebrahimi-Azandaryani, Omid Akbari, Mehdi Kamal, Ali Afzali-Kusha, Massoud Pedram

Publikováno v: ACM Transactions on Design Automation of Electronic Systems. 28:1-14

In this paper, two accuracy configurable adders capable of operating in approximate and exact modes are proposed. In the adders, which include a block-based carry propagate and a parallel prefix structure, the carry chains are cut off in the approxim

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::87a12d2f74516efd1b6b6df4690f171b
https://doi.org/10.1145/3549936

An Adaptive Memory-Side Encryption Method for Improving Security and Lifetime of PCM-Based Main Memory

Autor: Ali Afzali-Kusha, Morteza Soltani, Massoud Pedram, Mehdi Kamal

Publikováno v: IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems. 41:1744-1756

In this paper, we present a main memory system for improving the lifetime and security of phase-change main memories. Storing encrypted data increases the bit flip rates in memory cells which adversely affects the lifetime of the phase-change memory

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::68555fa49951802d22e9012f22abc86a
https://doi.org/10.1109/tcad.2021.3093832

Distributing DNN training over IoT edge devices based on transfer learning

Autor: Ali Afzali-Kusha, Ehsan Tanghatari, Massoud Pedram, Mehdi Kamal

Publikováno v: Neurocomputing. 467:56-65

In this paper, an approach for distributing the deep neural network (DNN) training onto IoT edge devices is proposed. The approach results in protecting data privacy on the edge devices and decreasing the load on cloud servers. In addition, the techn

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::76dd9feae5313ae61168e44f7ff5b922
https://doi.org/10.1016/j.neucom.2021.09.045

ReMeCo: Reliable Memristor-Based in-Memory Neuromorphic Computation

Autor: Ali BanaGozar, Seyed Hossein Hashemi Shadmehri, Sander Stuijk, Mehdi Kamal, Ali Afzali-Kusha, Henk Corporaal

Publikováno v: ASPDAC '23: Proceedings of the 28th Asia and South Pacific Design Automation Conference, 396-401
STARTPAGE=396;ENDPAGE=401;TITLE=ASPDAC '23

Memristor-based in-memory neuromorphic computing systems promise a highly efficient implementation of vector-matrix multiplications, commonly used in artificial neural networks (ANNs). However, the immature fabrication process of memristors and circu

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::28b97857df4ace7e82c92f85941c683e
https://research.tue.nl/nl/publications/59592ba4-a40d-4078-bda7-fe570c479da7