The role of data in building AI systems has recently been significantly magnified by the
emerging concept of data-centric AI (DCAI), which advocates a fundamental shift from model …

The emerging field of artificial intelligence of things (AIoT, AI+ IoT) is driven by the
widespread use of intelligent infrastructures and the impressive success of deep learning …

Chip floorplanning is the engineering task of designing the physical layout of a computer
chip. Despite five decades of research, chip floorplanning has defied automation, requiring …

In this work, we present a learning-based approach to chip placement, one of the most
complex and time-consuming stages of the chip design process. Unlike prior methods, our …

We propose TopoOpt, a novel direct-connect fabric for deep neural network (DNN) training
workloads. TopoOpt co-optimizes the distributed training process across three dimensions …

This paper proposes optical network interconnects as a key enabler for building high-
bandwidth ML training clusters with strong scaling properties. Our design, called SiP-ML …

Finding the best way to schedule operations in a computation graph is a classical NP-hard
problem which is central to compiler optimization. However, evaluating the goodness of a …

The application of deep reinforcement learning (DRL) to computer and networked systems
has recently gained significant popularity. However, the obscurity of decisions by DRL …

Accurate hardware performance models are critical to efficient code generation. They can be
used by compilers to make heuristic decisions, by superoptimizers as a minimization …

The rapid increase in sizes of state-of-the-art DNN models, and consequently the increase in
the compute and memory requirements of model training, has led to the development of …