Research
Driving advances in hardware, devices, and cyber-physical systems that expand the possibilities of computing.
We have built tools that help us accurately measure hardware performance metrics and have also developed machine-learning models that have enabled us to unravel the relationships between workloads and firmware settings.
The SIDH v2.0 library is a supersingular isogeny-based cryptography library that implements a new suite of algorithms for a post-quantum, ephemeral Diffie-Hellman key exchange scheme.
The Picnic and Fish signature schemes are digital signature schemes secure against attacks by quantum computers. This is a reference implementation of these schemes, associated with the paper Post-Quantum Zero-Knowledge and Signatures from Symmetric-Key Primitives.
Breakthroughs in Artificial Intelligence (AI) have typically shown that AI systems are good at solving specific tasks that have a well-defined goal, such as Speech Recognition, Image Captioning, Games like Poker/Go/Jeopardy, among others. However, as…
By Doug Burger, Distinguished Engineer, Microsoft Today at Hot Chips 2017, our cross-Microsoft team unveiled a new deep learning acceleration platform, codenamed Project Brainwave. I’m delighted to share more details in this post, since Project…
When attacking the problem of Autonomous Driving, one must take into account strict computational constraints, posed by the desired low cost of sensors and processors, and by the required real-time performance. Specifically, when considering Driving…
Predictive models are finding an increasing number of applications in many industries. As a result, a practical means for trading-off the cost of deploying a model versus its effectiveness is needed. Our work is motivated…
Deep neural networks (DNNs) have led to significant improvements to the accuracy of machine-learning applications. For many problems, such as object classification and object detection, DNNs have led to levels of accuracy that are acceptable…
