Results 71 to 80 of about 1,316 (216)

Triple‐Level Content‐Addressable Vertical NAND Flash Memory

Advanced Science, EarlyView.
Commercial triple‐level‐cell V‐NAND flash memory is transformed into a compact content‐addressable memory by directly exploiting its eight threshold‐voltage states. A parity‐split search scheme preserves storage density while enabling reliable multi‐level and don't‐care matching. Parallel word‐line search delivers up to 24‐fold higher throughput and 54.
Sung‐Ho Park, Jiseong Im, Yeongheon Yang, Ryun‐Han Koo, Jonghyun Ko, Mingyun Oh, Dongbeen Shin, Gyuweon Jung, Jong‐Ho Lee   +8 more
wiley   +1 more source

A note on data-parallelism and (and-parallel) prolog

, 1994
Abstract is not available.
Hermenegildo, Manuel V., Carro Liñares, Manuel   +1 more
openaire   +2 more sources

RRAM Variability Harvesting for CIM‐Integrated TRNG

Advanced Electronic Materials, EarlyView.
This work demonstrates a compute‐in‐memory‐compatible true random number generator that harvests intrinsic cycle‐to‐cycle variability from a 1T1R RRAM array. Parallel entropy extraction enables high‐throughput bit generation without dedicated circuits. This approach achieves NIST‐compliant randomness and low per‐bit energy, offering a scalable hardware
Ankit Bende, Gokulnath Rajendran, Regina Dittmann, Anupam Chattopadhyay, Vikas Rana   +4 more
wiley   +1 more source

Study on Distributed Training Optimization Based on Hybrid Parallel [PDF]

Jisuanji kexue
Large-scale neural network training is a hot topic in the field of deep learning,and distributed training stands out as one of the most effective methods for training large neural networks across multiple nodes.Distributed training typically involves ...
XU Jinlong, LI Pengfei, LI Jianan, CHEN Biaoyuan, GAO Wei, HAN Lin
doaj   +1 more source

Fundamental Challenges, Physical Implementations, and Integration Strategies for Ising Machines in Large‐Scale Optimization Tasks

Advanced Electronic Materials, EarlyView.
Ising machines are emerging as specialized hardware solvers for computationally hard optimization problems. This review examines five major platforms—digital CMOS, analog CMOS, emerging devices, coherent optics, and quantum systems—highlighting physics‐rooted advantages and shared bottlenecks in scalability and connectivity.
Hyunjun Lee, Joon Pyo Kim, Sanghyeon Kim
wiley   +1 more source

Electrode‐Engineered Dual‐Mode Multifunctional Lead‐Free Perovskite Optoelectronic Memristors for Neuromorphic Computing

Advanced Electronic Materials, EarlyView.
A lead‐free perovskite memristive solar cell structure that call emulate both synaptic and neuronal functions controlled by light and electric fields depending on top electrode type. ABSTRACT Memristive devices based on halide perovskites hold strong promise to provide energy‐efficient systems for the Internet of Things (IoT); however, lead (Pb ...
Michalis Loizos, Konstantinos Rogdakis, Konstantinos Chatzimanolis, Katerina Anagnostou, Emmanuel Kymakis   +4 more
wiley   +1 more source

Data Parallelism in Java

, 1998
Java supports threads and remote method invocation, but it does not support either data-parallel or distributed programming. This paper discusses Java’s shortcomings with respect to data-parallel programming and presents workarounds. The technical contributions of this paper are twofold: a source-to-source transformation that maps foral l statements ...
openaire   +2 more sources

On the Role of Preprocessing and Memristor Dynamics in Reservoir Computing for Image Classification

Advanced Electronic Materials, EarlyView.
ABSTRACT Reservoir computing (RC) is an emerging recurrent neural network architecture that has attracted growing attention for its low training cost and modest hardware requirements. Memristor‐based circuits are particularly promising for RC, as their intrinsic dynamics can reduce network size and parameter overhead in tasks such as time‐series ...
Rishona Daniels, Duna Wattad, Ronny Ronen, David Saad, Shahar Kvatinsky   +4 more
wiley   +1 more source

A Pipeline-Based ODE Solving Framework

IEEE Access
The traditional parallel solving methods of ordinary differential equations (ODE) are mainly classified into task-parallelism, data-parallelism, and instruction-level parallelism.
Ruixia Cao, Shangjun Hou, Lin Ma
doaj   +1 more source

Experimental Demonstration of Temporally Aware Fault‐Tolerant Sensor Fusion Using Memristive Associative Learning

Advanced Electronic Materials, EarlyView.
In dynamic driving scenarios, the proposed approach ensures only temporally aligned sensor inputs to make driving decisions, preventing false activations. By enabling selective hardware‐level learning, it achieves fast, reliable responses under noisy conditions.
Kapil Bhardwaj, Ashfak Nehal, Dmitrii Semenov, Roman Sotner, Sayani Majumdar   +4 more
wiley   +1 more source