Catastrophic Forgetting is a fundamental challenge faced by all systems that learn online. We propose leveraging the cognitive inspiration of context-dependent learning to reduce forgetting in a resource-efficient manner. Read more
We study the potential of leveraging human brain inspired principles of learning to build resource-efficient, accurate and adaptable ML solutions for memory management, specifically, memory access prefetching in computer systems. Read more
Computer systems are becoming increasingly heterogeneous. In heterogeneous systems, accelerators such as GPUs have lower memory capacity but higher bandwidth than CPUs. Furthermore, the interconnects between the heterogenous nodes are slower, slowing down memory transfer. To reduce these slow memory transfers, it is important to keep the data close to the node where it will be required. Read more
Today’s systems are increasingly heterogeneous. Accelerators such as GPUs and CPUs should all be viewed as available thread-pools capable of running the application at hand. The goal is not to outperform a discrete accelerator such as a GPU. The goal is to reduce idle system times, which leads to improved power efficiency and throughput for the entire heterogeneous ecosystem. Read more
Cognitive models are time-consuming to create because they require the combination of numerous types of computational tasks. They perform poorly because they are typically written in high-level languages such as Python. In this work, we present Distill, a domain-specific compilation tool for accelerating cognitive models while still allowing cognitive scientists to develop models in flexible high-level languages. Read more