Modern computer systems need to execute under strict safety constraints (eg, a power limit),
but doing so often conflicts with their ability to deliver high performance (ie minimal latency) …

Modern computer systems need to execute under strict safety constraints (eg, power limit),
but doing so often conflicts with their ability to deliver high performance (ie, minimal latency) …

Sample-efficient machine learning (SEML) has been widely applied to find optimal latency
and power tradeoffs for configurable computer systems. Instead of randomly sampling from …

Many modern computing systems must provide reliable latency with minimal energy. Two
central challenges arise when allocating system resources to meet these conflicting …

Computing systems such as processors are generally constrained in their resources like
power consumption, energy consumption, heat dissipation, and chip area. This makes …

While hardware accelerators provide significant performance and energy improvements
over general-purpose processors, their limited reusability incurs high design costs. It is thus …

Trusted execution environments (TEEs) for machine learning accelerators are indispensable
in secure and efficient ML inference. Optimizing workloads through state-space exploration …

Machine learning inference is increasingly being executed locally on mobile and embedded
platforms, due to the clear advantages in latency, privacy and connectivity. In this paper, we …

Creating a model of a computer system that can be used for tasks such as predicting future
resource usage and detecting anomalies is a challenging problem. Most current systems …

With transistor scaling nearing atomic dimensions and leakage power dissipation imposing
strict energy limitations, it has become increasingly difficult to improve energy efficiency in …