We share the experiences of four of our colleagues, offering professional advice for those starting their career, and aiding understanding of what it's like to be a women in engineering.

Our research explores how adding new explicit instructions which define their fine-grain ordering requirements could reduce the need for memory barriers.

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.

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.

Arm Research is excited to be part of the Cambridge ELLIS Unit, focusing on two key elements: Bayesian statistics and Probabilistic ML.

Our approach to creating a shared Edge computing framework borrows from the cloud, where a user allocates resources in each physical node it requests access to.

With the proliferation of smart devices, we need to make sure we are protecting our data. But have you ever considered the threat to the hardware supply chain?

Veracruz explores how novel, data-intensive distributed systems can be built using Strong Isolation Technologies and remote attestation.

Looking at how to make computers fundamentally more secure, the University of Cambridge with DARPA created CHERI, making compartmentalization more efficient and scalable.

Hardware-based coherence has simplified concurrent programming, and our work shows the improvements in performance and programmability.

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.

The Barcelona Supercomputing Center – Arm Research Centre of Excellence was formed to recognize the leadership and hard work in pioneering Arm in HPC.

Project Trishul demonstrates the feasibility and readiness of high-density, face-to-face, wafer-bonded 3D stacking technologies for high performance, energy-efficient designs.

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.

As part of DARPA’s Domain-Specific System on Chip program, Arizona State University assembled a team of academics and industry partners to pioneer a new approach to software-enabled radio frequency.

Designing any power-converter is a matter of compromises. For M0N0, we focused on efficiency at an extremely low output power - but how?

The University of Bristol is committed to making a positive impact in society via their research. What are our sponsored students working on?

The University of Washington’s Processing Systems Lab is pushing the boundaries of energy-efficient compute. Visvesh Sathe explains why Arm Research is a perfect partner.

Our collaboration with the University of Edinburgh supports fundamental research questions across a wide range of computer science.

We have implemented our own LLVM-based ahead-of-time Wasm compiler, named aWsm, offering configurable sandboxing, optimized for performance.

2020 has been an undeniably unique year. In Arm Research, we are taking a look back at some of our favorite blogs from the past year.