Overview of Recent Advancements in Deep Learning and Artificial Intelligence
Artificial intelligence (AI) systems have made significant impact on the society in the recent
years in a wide range of fields, including healthcare, transportation, and finances. In …
years in a wide range of fields, including healthcare, transportation, and finances. In …
GraphRC: Accelerating graph processing on dual-addressing memory with vertex merging
Architectural innovation in graph accelerators attracts research attention due to foreseeable
inflation in data sizes and the irregular memory access pattern of graph algorithms …
inflation in data sizes and the irregular memory access pattern of graph algorithms …
In-Memory Computing for AI Accelerators: Challenges and Solutions
Abstract In-memory computing (IMC)-based hardware reduces latency as well as energy
consumption for compute-intensive machine learning (ML) applications. Till date, several …
consumption for compute-intensive machine learning (ML) applications. Till date, several …
[HTML][HTML] End-to-End Benchmarking of Chiplet-Based In-Memory Computing
Abstract In-memory computing (IMC)-based hardware reduces latency and energy
consumption for compute-intensive machine learning (ML) applications. Several …
consumption for compute-intensive machine learning (ML) applications. Several …
Energy-Efficient In-Memory Acceleration of Deep Neural Networks Through a Hardware-Software Co-Design Approach
G Krishnan - 2022 - search.proquest.com
Deep neural networks (DNNs), as a main-stream algorithm for various AI tasks, achieve
higher accuracy at the cost of increased computational complexity and model size, posing …
higher accuracy at the cost of increased computational complexity and model size, posing …