tag: in-memory computing

OTA On-Chip Computing That Conquers A Bottleneck In Wired NoC Architectures
By Technical Paper Link - 21 Jun, 2022 - Comments: 0

New research paper titled "Wireless On-Chip Communications for Scalable In-memory Hyperdimensional Computing" from researchers at IBM Research, Zurich Switzerland and Universitat Politecnica de Catalunya, Barcelona, Spain Abstract: "Hyperdimensional computing (HDC) is an emerging computing paradigm that represents, manipulates, and communicates data using very long random vectors (aka hyp... » read more

Simulation Framework to Evaluate the Feasibility of Large-scale DNNs based on CIM Architecture & Analog NVM
By Technical Paper Link - 10 Jun, 2022 - Comments: 0

Technical paper titled "Accuracy and Resiliency of Analog Compute-in-Memory Inference Engines" from researchers at UCLA. Abstract "Recently, analog compute-in-memory (CIM) architectures based on emerging analog non-volatile memory (NVM) technologies have been explored for deep neural networks (DNNs) to improve scalability, speed, and energy efficiency. Such architectures, however, leverage ... » read more

Analog Edge Inference with ReRAM
By Technical Paper Link - 02 May, 2022 - Comments: 0

Abstract "As the demands of big data applications and deep learning continue to rise, the industry is increasingly looking to artificial intelligence (AI) accelerators. Analog in-memory computing (AiMC) with emerging nonvolatile devices enable good hardware solutions, due to its high energy efficiency in accelerating the multiply-and-accumulation (MAC) operation. Herein, an Applied Materials... » read more

Nonvolatile Capacitive Crossbar Array for In-Memory Computing
By Technical Paper Link - 29 Mar, 2022 - Comments: 0

Abstract "Conventional resistive crossbar array for in-memory computing suffers from high static current/power, serious IR drop, and sneak paths. In contrast, the “capacitive” crossbar array that harnesses transient current and charge transfer is gaining attention as it 1) only consumes dynamic power, 2) has no DC sneak paths and avoids severe IR drop (thus, selector-free), and 3) can be f... » read more

A crossbar array of magnetoresistive memory devices for in-memory computing
By Technical Paper Link - 14 Jan, 2022 - Comments: 0

Samsung has demonstrated the world’s first in-memory computing technology based on MRAM. Samsung has a paper on the subject in Nature. This paper showcases Samsung’s effort to merge memory and system semiconductors for next-generation artificial intelligence (AI) chips. Abstract "Implementations of artificial neural networks that borrow analogue techniques could potentially offer low-po... » read more

Toward Software-Equivalent Accuracy on Transformer-Based Deep Neural Networks With Analog Memory Devices
By Technical Paper Link - 20 Dec, 2021 - Comments: 0

Abstract:  "Recent advances in deep learning have been driven by ever-increasing model sizes, with networks growing to millions or even billions of parameters. Such enormous models call for fast and energy-efficient hardware accelerators. We study the potential of Analog AI accelerators based on Non-Volatile Memory, in particular Phase Change Memory (PCM), for software-equivalent accurate i... » read more

Enabling Training of Neural Networks on Noisy Hardware
By Technical Paper Link - 18 Nov, 2021 - Comments: 0

Abstract:  "Deep neural networks (DNNs) are typically trained using the conventional stochastic gradient descent (SGD) algorithm. However, SGD performs poorly when applied to train networks on non-ideal analog hardware composed of resistive device arrays with non-symmetric conductance modulation characteristics. Recently we proposed a new algorithm, the Tiki-Taka algorithm, that overcomes t... » read more

Accelerating Inference of Convolutional Neural Networks Using In-memory Computing
By Technical Paper Link - 17 Nov, 2021 - Comments: 0

Abstract: "In-memory computing (IMC) is a non-von Neumann paradigm that has recently established itself as a promising approach for energy-efficient, high throughput hardware for deep learning applications. One prominent application of IMC is that of performing matrix-vector multiplication in (1) time complexity by mapping the synaptic weights of a neural-network layer to the devices of a... » read more

Accelerating Inference of Convolutional Neural Networks Using In-memory Computing
By Technical Paper Link - 09 Nov, 2021 - Comments: 0

Abstract: "In-memory computing (IMC) is a non-von Neumann paradigm that has recently established itself as a promising approach for energy-efficient, high throughput hardware for deep learning applications. One prominent application of IMC is that of performing matrix-vector multiplication in (1) time complexity by mapping the synaptic weights of a neural-network layer to the devices of an ... » read more

11 Ways To Reduce AI Energy Consumption
By Bryon Moyer - 13 May, 2021 - Comments: 1

As the machine-learning industry evolves, the focus has expanded from merely solving the problem to solving the problem better. “Better” often has meant accuracy or speed, but as data-center energy budgets explode and machine learning moves to the edge, energy consumption has taken its place alongside accuracy and speed as a critical issue. There are a number of approaches to neural netw... » read more

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