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A survey on memory subsystems for deep neural network accelerators
From self-driving cars to detecting cancer, the applications of modern artificial intelligence
(AI) rely primarily on deep neural networks (DNNs). Given raw sensory data, DNNs are able …
(AI) rely primarily on deep neural networks (DNNs). Given raw sensory data, DNNs are able …
Processing Multi-Layer Perceptrons In-Memory
Important modern applications such as machine learning, deep learning, graph processing,
databases (and many others) are memory-bound. This creates a bottleneck caused by the …
databases (and many others) are memory-bound. This creates a bottleneck caused by the …
Custom Memory Design for Logic-in-Memory: Drawbacks and Improvements over Conventional Memories
The speed of modern digital systems is severely limited by memory latency (the “Memory
Wall” problem). Data exchange between Logic and Memory is also responsible for a large …
Wall” problem). Data exchange between Logic and Memory is also responsible for a large …
Heterogeneous Uncore Architectures in Future Chip-Multi Processors
A Asad - rshare.library.torontomu.ca
Uncore components including on-chip memory systems and interconnects consume a
significant portion of overall energy consumption in emerging embedded applications …
significant portion of overall energy consumption in emerging embedded applications …
[PDF][PDF] Custom Memory Design for Logic-in-Memory: Drawbacks and Improvements over Conventional Memories. Electronics 2021, 10, 2291
The speed of modern digital systems is severely limited by memory latency (the “Memory
Wall” problem). Data exchange between Logic and Memory is also responsible for a large …
Wall” problem). Data exchange between Logic and Memory is also responsible for a large …