Due to the increasing size of integrated circuits (ICs), their design and optimization phases
(ie, computer-aided design, CAD) grow increasingly complex. At design time, a large design …

It has been a long time that computer architecture and systems are optimized for efficient
execution of machine learning (ML) models. Now, it is time to reconsider the relationship …

Reinforcement learning algorithms have been very successful at solving sequential decision-
making problems in many different problem domains. However, their training is often time …

This paper presents a comprehensive survey of the state-of-the-art work on fault tolerance
methods proposed for cloud computing. The survey classifies fault-tolerance methods into …

Mobile and IoT devices have proliferated our daily lives. However, these miniaturized
computing systems should be highly energy-efficient due to their ultrasmall form factor …

The emergence of ML in various cloud system management tasks (eg, workload autoscaling
and job scheduling) has become a core driver of ML-centric cloud platforms. However, there …

The complexity of heterogeneous mobile platforms is growing at a rate faster than our ability
to manage them optimally at runtime. For example, state-of-the-art systems-on-chip (SoCs) …

Mobile platforms must satisfy the contradictory requirements of fast response time and
minimum energy consumption as a function of dynamically changing applications. To …

Manycore chips are widely employed in high-performance computing and large-scale data
analysis. However, the design of high-performance manycore chips is dominated by power …

Heterogeneous multiprocessor system-on-chips (SoCs) provide a wide range of parameters
that can be managed dynamically. For example, one can control the type (big/little), number …