The Case for In-Memory Inferencing of Quantized CNNs at the Edge
preprintposted on 07.03.2022, 03:04 by Gabriel Falcao, João Dinis FerreiraJoão Dinis Ferreira
As artiﬁcial intelligence becomes a pervasive tool for the billions of IoT devices at the edge, the data movement bottleneck imposes severe limitations on these systems’ performance and autonomy. Processing-in-Memory emerges as a way to mitigate the data movement bottleneck while satisfying the stringent performance, energy efﬁciency, and accuracy requirements of edge imaging applications that rely on convolutional neural networks.
Hardware Accelerated Deep LEarning framework
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