Open AccessPosted Content
Mixed-Precision 'Memcomputing'
Manuel Le Gallo,Abu Sebastian,Roland Mathis,Matteo Manica,Tomas Tuma,Costas Bekas,Alessandro Curioni,Evangelos Eleftheriou +7 more
TL;DR: The concept of mixed-precision 'memcomputing' that combines a von Neumann machine with a 'memcomputer' in a hybrid system that benefits from both the high precision of conventional computing and the energy/areal efficacy of ' Memcomputing is proposed.
read more
Abstract: To process the ever-increasing amounts of data, computing technology has relied upon the laws of Dennard and Moore to scale up the performance of conventional von Neumann machines. As these laws break down due to technological limits, a radical departure from the processor-memory dichotomy is needed to circumvent the limitations of today's computers. 'Memcomputing' is a promising concept in which the physical attributes and state dynamics of nanoscale resistive memory devices are exploited to perform computational tasks with collocated memory and processing. The capability of 'memcomputing' for performing certain logical and arithmetic operations has been demonstrated. However, device variability and non-ideal device characteristics pose technical challenges to reach the numerical accuracy usually required in practice for data analytics and scientific computing. To resolve this, we propose the concept of mixed-precision 'memcomputing' that combines a von Neumann machine with a 'memcomputer' in a hybrid system that benefits from both the high precision of conventional computing and the energy/areal efficacy of 'memcomputing'. Such a system can achieve arbitrarily high computational accuracy with the bulk of the computation realized as low-precision 'memcomputing'. We demonstrate this by addressing the problem of solving systems of linear equations and present experimental results of solving accurately a system of 10,000 equations using 959,376 phase-change memory devices. We also demonstrate a practical application of computing the gene interaction network from RNA expression measurements. These results illustrate that an interconnection of high-precision arithmetic and 'memcomputing' can be used to solve problems at the core of today's computing applications.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
In-memory computing on a photonic platform
Carlos Ríos,Nathan Youngblood,Zengguang Cheng,Manuel Le Gallo,Wolfram H. P. Pernice,C. David Wright,Abu Sebastian,Harish Bhaskaran +7 more
TL;DR: In this paper, the authors combine integrated optics with collocated data storage and processing to enable all-photonic in-memory computations, which can leverage the increased speed and bandwidth potential of the optical domain and remove the need for electro-optical conversions.
353
•Posted Content
In-memory computing on a photonic platform
Carlos Ríos,Nathan Youngblood,Zengguang Cheng,Manuel Le Gallo,Wolfram H. P. Pernice,C. David Wright,Abu Sebastian,Harish Bhaskaran +7 more
TL;DR: In this paper, a single-shot Write/Erase and a drift-free process are used to enable all-photonic in-memory computations on single devices using nonvolatile photonic elements based on the phase change material, Ge2Sb2Te5.
6
Computation-in-Memory based on Memristive Devices
H.A. Du Nguyen
- 13 Sep 2019
TL;DR: This thesis focuses on exploring and developing in-memory computing in terms of architectures (including classification, limited schemes of instruction set, micro-architecture, communication and controller, as well as automation and simulator), and circuits (including logic synthesis flow and interconnect network schemes).
5
Hardware emulation of phase change memory
Anastasios Petropoulos,Theodore Antonakopoulos +1 more
- 01 Nov 2017
TL;DR: This work presents the basic unit of such a hardware emulator and the architecture of a tool that can be used to accurately emulate the memory characteristics of phase-change memory.
2
Unsupervised Learning of Phase-Change-Based Neuromorphic Systems
Stanislaw Wozniak
- 01 Jan 2017
TL;DR: This dissertation proposes phase- change-based neuromorphic architectures based on phase-change memristors combined with biologically-inspired synaptic learning rules, and experimentally demonstrates their pattern- and feature-learning capabilities.
1