While machine learning has made tremendous progress in recent years, there is still a large gap between artificial and natural intelligence.
Closing this gap requires combining fundamental research in neuroscience with mathematics, physics, and engineering to understand the principles of neural computation and cognition.
Mixed-signal subthreshold analog and asynchronous digital electronic...
The real-time processing of data created by the Large Hadron Collider's (LHC) experiments, amounting to over 10% of worldwide internet traffic, is one of the greatest computing challenges ever attempted. I will discuss the concrete applications of real-time processing in the LHC's main experiments, and the technological innovations in this area over the past decades. I will also reflect on the...
This talk provides an overview of several libraries in the open-source JAX ecosystem (such as Equinox, Diffrax, Optimistix, ...) In short, we have been building an "autodifferentiable GPU-capable scipy". These libraries offer the foundational core of tools that have made it possible for us to train neural networks (e.g. score-based diffusions for image generation), solve PDEs, and smoothly...
Most commercial wearables still capture only basic metrics such as step counts or heart rate, and remain closed systems without access to raw data. In this talk, I will present our holistic approach to full-body biosignal intelligence, where ultra-low-power embedded platforms and machine learning algorithms are co-designed to capture and process signals from the brain, eyes, muscles, and...
From radio telescopes to particle accelerators and electron microscopes, scientific instruments produce tremendous amounts of data at equally high rates; previous architectures that have relied on offline storage and large data transfers are unable to keep up. The future of scientific discovery is interactive, streaming, and AI driven, placing the autonomous and intelligent instrument at the...
AI is accelerating into the generative era, and it is poised to disrupt multiple businesses and applications. With the increasing focus on edge and extreme-edge, near sensor applications, inference is becoming the key workload and computational challenge. Computing system need to scale out and scale up to meet the challenge. In this talk I will discuss how to scale up chip(lets) for efficient...
Beyond the well-known highlights in computer vision and natural language, AI is steadily expanding into new application domains. This Pervasive AI trend requires supporting diverse and fast-moving application requirements, ranging from specialized I/O to fault tolerance and limited resources, all the while retaining high performance and low latency. Adaptive compute architectures such as AMD...
Decision Forests such as Random Forests and Gradient Boosted Trees are an effective and widely used class of models for machine learning, particularly for tabular data and forecasting. This talk covers the practical use and ongoing research on Decision Forests at Google. We provide a brief overview of decision forest modeling with a focus on novel split conditions. We will analyze their impact...
Graph Neural Networks (GNNs) are a powerful paradigm for Neural Net ML models to operate on relational data or data with structural information. This talk explores the practical use and ongoing research on GNN done at Google for industrial applications. We provide a brief overview of GNNs modeling, including GCNs, Graph Transformers, and geometric-aware models. Then we discuss a variety of...
