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POWER-EFFICIENT DEEP NEURAL NETWORK MODULE CONFIGURED FOR EXECUTING A LAYER DESCRIPTOR LIST
POWER-EFFICIENT DEEP NEURAL NETWORK MODULE CONFIGURED FOR EXECUTING A LAYER DESCRIPTOR LIST
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机译：高效的深层神经网络模块，用于执行层描述符列表
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摘要
A deep neural network (DNN) processor is configured to execute descriptors in layer descriptor lists. The descriptors define instructions for performing a pass of a DNN by the DNN processor. Several types of descriptors can be utilized: memory-to-memory move (M2M) descriptors; operation descriptors; host communication descriptors; configuration descriptors; branch descriptors; and synchronization descriptors. A DMA engine uses M2M descriptors to perform multi-dimensional strided DMA operations. Operation descriptors define the type of operation to be performed by neurons in the DNN processor and the activation function to be used by the neurons. M2M descriptors are buffered separately from operation descriptors and can be executed at soon as possible, subject to explicitly set dependencies. As a result, latency can be reduced and, consequently, neurons can complete their processing faster. The DNN module can then be powered down earlier than it otherwise would have, thereby saving power.
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