AIMC - An In-Memory Computing Solution that Promises to Revolutionize AI

Category Electronics

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IBM Research Europe recently developed an in-memory computing chip powered by phase-change memory devices. This chip was tested on deep learning datasets and was able to attain 99% accuracy while reducing energy consumption and computational times by over 50%.

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For decades, electronics engineers have been trying to develop increasingly advanced devices that can perform complex computations faster and consuming less energy. This has become even more salient after the advent of artificial intelligence (AI) and deep learning algorithms, which typically have substantial requirements both in terms of data storage and computational load.A promising approach for running these algorithms is known as analog in-memory computing (AIMC). As suggested by its name, this approach consists of developing electronics that can perform computations and store data on a single chip. To realistically achieve both improvements in speed and energy consumption, this approach should ideally also support on-chip digital operations and communications.

The chip developed by IBM Research Europe consists of 64 analog PCM-Based cores.

Researchers at IBM Research Europe recently developed a new 64-core mixed-signal in-memory computing chip based on phase-change memory devices that could better support the computations of deep neural networks. Their 64-core chip, presented in a paper in Nature Electronics, has so far attained highly promising results, retaining the accuracy of deep learning algorithms, while reducing computation times and energy consumption.

Each core is connected via a digital communication network.

"We have been investigating how to use phase-change memory (PCM) devices for computing for more than 7 years, starting from when we first showed how to implement neuronal functions with individual PCM devices," Manuel Le Gallo, one of the authors of the paper, told Tech Xplore. "Since then we showed that a lot of applications could benefit from using PCM devices as compute elements, such as scientific computing and deep neural network inference, for which we demonstrated little to no accuracy loss in hardware/software implementations using prototype PCM chips. With this new chip, we wanted to go a step forward towards an end-to-end analog AI inference accelerator chip." .

The chip is capable of reducing computation times and power consumption by over 50%.

To create their new in-memory computing chip, Le Gallo and his colleagues combined PCM-based cores with digital computing processors, connecting all cores and digital processing units via an on-chip digital communication network. Their chip consists of 64 analog PCM-based cores, each of which contains a 256-by-256 crossbar array of synaptic unit cells.

"We integrated compact, time-based analog-to-digital converters in each core to transition between the analog and digital worlds," Le Gallo explained. "Each core is also integrated with lightweight digital processing units that perform rectified linear unit (reLU) neuronal activation functions and scaling operations. A global digital processing unit is integrated in the middle of the chip that implements long-short term memory (LSTM) network operations." .

This chip uses 256-by-256 crossbar array of synaptic unit cells.

A unique characteristic of the team's chip is that the memory cores contained inside it and its global processing unit are connected via a digital communication network. This allows it to perform all computations associated with individual layers of a neural network on-chip, significantly reducing computation times and power consumption.

To evaluate their chip, Le Gallo and his colleagues carried out a highly comprehensive study, running deep learning a few popular datasets. In each case, their chip was able to correctly classify images correctly at a 99% accuracy rate, while reducing the energy consumption by over 50% compared to traditional digital processing approaches.

The chip is capable of correctly classifying 99% of correctly classified images.

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