Arm ML Research Lab

Machine learning is finding increasing application across all compute tasks. Our research enables the ubiquitous application of energy-efficient machine learning by developing advanced hardware, software, and algorithms for this rapidly evolving area.
Accelerate Machine Learning

Our researchers are developing advanced, energy efficient hardware, software, and tools to support complex algorithms. Explore the Arm Research Impact Report 2021 to learn more.

More blogs

Machine Learning

TinyML: Ubiquitous embedded intelligence

Becky Ellis

With Arm’s vast microprocessor ecosystem at its foundation, the world is entering a new era of Tiny ML. Professor Vijay Janapa Reddi walks us through this emerging field.
- November 28, 2024
Fast and accurate keyword spotting using Transformers

Axel Berg

On-device automatic speech recognition is now becoming feasible and is useful in scenarios without internet connection or when data privacy is a concern.
- January 10, 2022
FixyFPGA: Fully-parallel and fully-pipelined FPGA accelerator for sparse CNNs

Jae-sun Seo

Most conventional FPGA-based accelerators use off-chip memory for data transformation, then perform computation for a single-layer in a time-multiplexed manner. Throughput is often limited by the memory…
- September 28, 2021
Neural network architectures for deploying TinyML applications on commodity microcontrollers

Colby Banbury

TinyML seeks to deploy ML algorithms on ultra low power systems, to enable us to intelligently select which data to transmit, improving energy efficiency.
- June 29, 2021
Improving federated learning with dynamic regularization

Paul Whatmough

IoT devices collect and transmit data to the cloud, where it is then analyzed and ML models trained. Privacy challenges arise when users are reluctant to share their personal data.
- June 11, 2021
What you missed at the second On-Device Intelligence Workshop

Paul Whatmough

The workshop held in conjunction with MLSys 2021 brought researchers and practitioners together to discuss key issues, share new research results and practical tutorial material.
- June 3, 2021
Arm Research’s collaboration with the Cambridge ELLIS Unit

Partha Maji

Arm Research is excited to be part of the Cambridge ELLIS Unit, focusing on two key elements: Bayesian statistics and Probabilistic ML.
- May 21, 2021
Ensuring your AI is sure: Any place, anywhere, anytime

Tiago Azevedo

It is important in industry to define what we see and how well we see it. This simple yet powerful idea has driven recent developments in the Arm Research ML Lab.
- April 9, 2021
Using multiple labels improves neural network learning

Axel Berg

A single label is not enough. Label diversity can be introduced by creating several labels for each training example in a way that the ordinal structure allows.
- February 22, 2021
Research for a sustainable future

René de Jong

To help companies find the breakthrough innovations needed to support the Global Goals, the UNGC set up the Young SDG Innovator Program, which our colleagues in Arm joined.
- October 19, 2020
Efficient Bug Discovery with Machine Learning for Hardware Verification

Hongsup Shin

For present-day microprocessors, it is even more challenging to identify bugs. Using (ML) to efficiently identify bugs, we've seen a 25% increase in efficiency than the default verification workflow. …
- September 22, 2020
Reducing the Cost of Neural Network Inference with Residue Number Systems

Matthew Mattina

The size and computational complexity of neural network models continues to grow exponentially. However, the increase in computational requirements presents a major challenge to their adoption. Could Residue…
- August 21, 2020
Adapting Models to the Real World: On-Device Training for Edge Model Adaptation

Mark O'Connor

Neural networks are becoming widely used in computer interaction, but in real-world scenarios we see errors. We’ve recently completed research into edge distillation to solve this problem.
- July 15, 2020
It is time for natively flexible processors

Emre Ozer

The story behind our flexible processors paper started with how to make billions of everyday things smart.
- July 13, 2020
Scalable Hyperparameter Tuning for AutoML

Mohit Aggarwal

Mango is an open source Python library for hyperparameter optimization, built for AutoML systems. Developed by Arm Research, Mango presents many useful features.
- July 7, 2020
Even Faster Convolutions: Winograd Convolutions meet Integer Quantization and Architecture Search

Javier Fernandez-Marques

The design of deep learning (DL) neural network (NN) models targeting mobile devices has advanced rapidly over the last couple of years. Important computer vision tasks have led a community-wide transition…
- April 29, 2020
SCALE-Sim: A cycle-accurate NPU simulator for your research experiments

Paul Whatmough

Architecture simulators are a key tool in the computer architecture toolbox. They provide a convenient model of real hardware at a level of abstraction that makes them faster and more flexible than low…
- April 21, 2020
TinyML Applications Require New Network Architectures

Urmish Thakker

Researchers have studied neural network compression for quite some time. However, the need for always on compute has led to a recent trend towards executing these applications on even smaller IoT devices…
- February 13, 2020