Machine-learned predictors, although achieving very good results for inputs resembling
training data, cannot possibly provide perfect predictions in all situations. Still, decision …

Past research has proposed numerous hardware prefetching techniques, most of which rely
on exploiting one specific type of program context information (eg, program counter …

Imitation learning (IL) aims to extract knowledge from human experts' demonstrations or
artificially created agents to replicate their behaviors. It promotes interdisciplinary …

The goal of meta-reinforcement learning (meta-RL) is to build agents that can quickly learn
new tasks by leveraging prior experience on related tasks. Learning a new task often …

Web applications rely heavily on software caches to achieve low-latency, high-throughput
services. To adapt to changing workloads, three types of learned caches (learned evictions) …

Hybrid storage systems (HSS) use multiple different storage devices to provide high and
scalable storage capacity at high performance. Data placement across different devices is …

In modern solid-state drives (SSDs), the indexing of flash pages is a critical component in
their storage controllers. It not only affects the data access performance, but also determines …

Memory buffer allocation for on-chip memories is a major challenge in modern machine
learning systems that target ML accelerators. In interactive systems such as mobile phones …

Video streaming services are among the largest web applications in production, and a large
source of downstream internet traffic. A large-scale video streaming service at Google …

With the explosive increase in mobile data traffic and stringent quality-of-experience
requirements of users, mobile edge caching is a promising paradigm to reduce delivery …