This paper explores the environmental impact of the super-linear growth trends for AI from a
holistic perspective, spanning Data, Algorithms, and System Hardware. We characterize the …

Precise hardware performance models play a crucial role in code optimizations. They can
assist compilers in making heuristic decisions or aid autotuners in identifying the optimal …

Real-time multi-task multi-model (MTMM) workloads, a new form of deep learning inference
workloads, are emerging for applications areas like extended reality (XR) to support …

Deep neural networks (DNNs) have been widely adopted for various mobile inference tasks,
yet their ever-increasing computational demands are hindering their deployment on …

Running multiple deep neural networks (DNNs) in parallel has become an emerging
workload in both edge devices, such as mobile phones where multiple tasks serve a single …

Bayesian optimization is a popular method for sample efficient multi-objective optimization.
However, existing Bayesian optimization techniques fail to effectively exploit common and …

Due to limited resources on edge and different characteristics of deep neural network (DNN)
models, it is a big challenge to optimize DNN inference performance in terms of energy …

Today, deep learning optimization is primarily driven by research focused on achieving high
inference accuracy and reducing latency. However, the energy efficiency aspect is often …

Applications of machine learning (ML) are growing by the day for many unique and
challenging scientific applications. However, a crucial challenge facing these applications is …

Benefited from expanding cloud infrastructure, today's neural networks have increasingly
high performance trained on the cloud. Model researchers spent months of sweat competing …