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The Case for In-Memory Inferencing of Quantized CNNs at the Edge
  • Gabriel Falcao ,
  • João Dinis Ferreira
Gabriel Falcao
Instituto de Telecomunicações

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João Dinis Ferreira
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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.