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Emerging Computing: From Devices to SystemsSynthesis and Technology Mapping for In-Memory Computing

Emerging Computing: From Devices to Systems: Synthesis and Technology Mapping for In-Memory... [In this chapter, we introduce the preliminaries of in-memory computing processing-in-memory platforms, such as memristive Memory Processing Units (mMPU), which allow leveraging data locality and performing stateful logic operations. To allow computing of arbitrary Boolean functions using such novel computing platforms, development of design automation flows (EDA) are of critical importance. Typically, EDA flows consist of multiple phases. Technology-independent logic synthesis is the first step, where the input Boolean function is restructured without any specific technology constraints, which is generally followed by a technology-dependent optimization phase, where technology specific hints are used for optimization of the data structure obtained from the first step. The final step is technology mapping, which takes the optimized function representation to implement it using technology-specific constraints. In this chapter, we present an end-to-end mapping framework for mMPU with various mapping objectives. We begin the chapter by presenting an optimal technology mapping method with the goal of mapping a Boolean function on a single row of mMPU. Thereafter, we propose a Look-Up Table (LUT) based mapping that attempts at minimizing delay of mapping, without any area constraints. We extend this method to work with area-constraints. The proposed framework is modular and can be improved with more efficient heuristics as well as technology-specific optimizations. We present benchmarking results with other approaches throughout this chapter.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

Emerging Computing: From Devices to SystemsSynthesis and Technology Mapping for In-Memory Computing

Part of the Computer Architecture and Design Methodologies Book Series
Editors: Aly, Mohamed M. Sabry; Chattopadhyay, Anupam
Bhattacharjee, Debjyoti; Chattopadhyay, Anupam
Emerging Computing: From Devices to Systems — Jul 9, 2022
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Publisher
Springer Nature Singapore
Copyright
© Springer Nature Singapore Pte Ltd. 2023. Chapters "Innovative Memory Architectures Using Functionality Enhanced Devices" and "Intelligent Edge Biomedical Sensors in the Internet of Things (IoT) Era" are licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/). For further details see license information in the chapters.
ISBN
978-981-16-7486-0
Pages
317 –353
DOI
10.1007/978-981-16-7487-7_10
Publisher site
See Chapter on Publisher Site

Abstract

[In this chapter, we introduce the preliminaries of in-memory computing processing-in-memory platforms, such as memristive Memory Processing Units (mMPU), which allow leveraging data locality and performing stateful logic operations. To allow computing of arbitrary Boolean functions using such novel computing platforms, development of design automation flows (EDA) are of critical importance. Typically, EDA flows consist of multiple phases. Technology-independent logic synthesis is the first step, where the input Boolean function is restructured without any specific technology constraints, which is generally followed by a technology-dependent optimization phase, where technology specific hints are used for optimization of the data structure obtained from the first step. The final step is technology mapping, which takes the optimized function representation to implement it using technology-specific constraints. In this chapter, we present an end-to-end mapping framework for mMPU with various mapping objectives. We begin the chapter by presenting an optimal technology mapping method with the goal of mapping a Boolean function on a single row of mMPU. Thereafter, we propose a Look-Up Table (LUT) based mapping that attempts at minimizing delay of mapping, without any area constraints. We extend this method to work with area-constraints. The proposed framework is modular and can be improved with more efficient heuristics as well as technology-specific optimizations. We present benchmarking results with other approaches throughout this chapter.]

Published: Jul 9, 2022

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