A research opens the way for the next generation of energy efficient AI devices

Energy Efficient AI devices/appliances for smart homes is what the researchers at University of Tokyo are working on. Data can be compressed, the use of hardware will be simplified and this will be an important step towards 'Internet of Things.'

Tokyo, A research undertaken by Institute of Industrial Science at the University of Tokyo involved designing and building of specialised computer hardware, consisting of stacks of memory modules arranged in a 3D-spiral for artificial intelligence (AI) applications. It may open the way for the next generation of energy efficient AI devices.

Machine learning is a type of AI that allows computers to be trained by example data to make predictions for new instances. For example, a smart speaker algorithm like Alexa can learn to understand your voice commands, so it can understand you even when you ask for something for the first time. However, AI tends to require a great deal of electrical energy to train, which raises concerns about adding to climate change.

This research involves having on-chip nonvolatile memory placed close to the processors makes the machine learning training process much faster and more energy efficient. This is because electrical signals have a much shorter distance to travel compared with conventional computer hardware. Stacking multiple layers of circuits is a natural step, since training the algorithm often requires many operations to be run in parallel at the same time.

"For these applications, each layer's output is typically connected to the next layer's input. Our architecture greatly reduces the need for interconnecting wiring," says first author Jixuan Wu.

The team tested the device using a common task in AI, interpreting a database of handwritten digits. Energy efficiency of the device increased as the parameters were restricted to be +1 or-1. (a system of binarized neural networks). This both greatly simplifies the hardware used, as well as compressing the amount of data that must be stored, increasing the accuracy of the algorithm, up to a maximum of around 90%.

"In order to keep energy consumption low as AI becomes increasingly integrated into daily life, we need more specialized hardware to handle these tasks efficiently," explains Senior author Masaharu Kobayashi.

This work is an important step towards the "internet of things," in which many small AI-enabled appliances communicate as part of an integrated "smart-home."