Apple’s M1 Chip is Exactly What Machine Learning Needs

AI at the edge.

Frederik Bussler
Nov 25, 2020 · 2 min read
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Photo by Grzegorz Walczak on Unsplash

Edge Computing

In a centralized machine learning network, users send data to a server, which makes a prediction, and sends that back to the user. This is slower, more expensive, less reliable, and less secure than edge computing, where predictions are made directly on the user’s device.

The problem with edge computing is that mobile and IoT devices are generally weak and low-powered, while AI models often have intense compute requirements.

Apple’s M1 chip is the answer.

M1 Chip

Using cutting-edge 5-nanometer process technology, the M1 is packed with 16 billion transistors. This doesn’t come at any cost to efficiency, and the latest Mac actually has up to 2x longer battery life.

M1’s Neural Engine

You’ve probably heard of another famous NPU out there: Google’s TPU, or Tensor Processing Unit.

The Implications of the M1 Chip

Centralized networks could simply be too slow to deploy these ever-heavier models. Devices with the M1 chip — currently the MacBook Air, Mac mini, and MacBook Pro — make the process of training and deploying AI models on-device much more feasible.

The A14 is a Close Runner-up

Instead, the iPhone 12 has what’s called the “A14 Bionic” chip, an 11.8-billion transistor powerhouse that has a fast neural engine, a new image signal processor, and 70% faster machine learning accelerators.

In conclusion, Apple has powerful AI capabilities across its product line, enabling more AI applications at the edge than ever before.

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Frederik Bussler

Written by

AutoML enthusiast and no-coder at Obviously.AI.

Data Driven Investor

empower you with data, knowledge, and expertise

Frederik Bussler

Written by

AutoML enthusiast and no-coder at Obviously.AI.

Data Driven Investor

empower you with data, knowledge, and expertise

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