A message-driven mapping framework for VGG-19 inference on the MAVeC accelerator is claimed to generate over 97% of messages on-chip and sustain 88-92% SiteO utilization in simulation.
Greedy Prefetch for Reducing Off -Chip Memory Accesses in Convolutional Neural Network Inference,
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Hardware-Aware Data and Instruction Mapping for AI Tasks: Balancing Parallelism, I/O and Memory Tradeoffs
A message-driven mapping framework for VGG-19 inference on the MAVeC accelerator is claimed to generate over 97% of messages on-chip and sustain 88-92% SiteO utilization in simulation.