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

A²P-MANN: Adaptive Attention Inference Hops Pruned Memory-Augmented Neural Networks

Autor: Mohsen Ahmadzadeh, Mehdi Kamal, Ali Afzali-Kusha, Massoud Pedram

Publikováno v: IEEE Transactions on Neural Networks and Learning Systems. :1-13

In this work, to limit the number of required attention inference hops in memory-augmented neural networks, we propose an online adaptive approach called A²P-memory-augmented neural network (MANN). By exploiting a small neural network classifier, an

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2f742c95ba40a46c51d8ffbb28d33531
https://doi.org/10.1109/tnnls.2022.3148818

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