CNN Hardware Architecture With Weights Generator Module That Alleviates Impact Of The Memory Wall

A technical paper titled “Mitigating Memory Wall Effects in CNN Engines with On-the-Fly Weights Generation” was published by researchers at Samsung AI Center and University of Cambridge. Abstract: "The unprecedented accuracy of convolutional neural networks (CNNs) across a broad range of AI tasks has led to their widespread deployment in mobile and embedded settings. In a pursuit for high... » read more

TU Dresden: Tile-based Multi-Core Architecture for Heterogeneous RISC-V Processors Suitable for FPGA Platforms

New technical paper titled "AGILER: An Adaptive Heterogeneous Tile-Based Many-Core Architecture for RISC-V Processors" from researchers at Technische Universitaet Dresden (TU Dresden). Partial Abstract: "In this work, AGILER is proposed as an adaptive tile-base many-core architecture for heterogeneous RISC-V based processors. The proposed architecture consists of modular and adaptable heter... » read more

Why Reconfigurability Is Essential For AI Edge Inference Throughput

For a neural network to run at its fastest, the underlying hardware must run efficiently on all layers. Through the inference of any CNN—whether it be based on an architecture such as YOLO, ResNet, or Inception—the workload regularly shifts from being bottlenecked by memory to being bottlenecked by compute resources. You can think of each convolutional layer as its own mini-workload, and so... » read more