Efficient Processing of Deep Neural Networks. Vivienne Sze

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      A DNN-centric version of Little’s Law would have throughput measured in inferences per second, latency measured in seconds, and inferences-in-flight, as the tasks-in-flight equivalent, measured in the number of images in a batch being processed simultaneously. This helps to explain why increasing the number of inferences in flight to increase throughput may be counterproductive because some techniques that increase the number of inferences in flight (e.g., batching) also increase latency.

⁵ By total operations we mean both effectual and ineffectual operations.

⁶ Here, an operation can be a MAC operation or a data movement.

⁷ There is also cost associated with operating a system, such as the electricity bill and the cooling cost, which are primarily dictated by the energy efficiency and power consumption, respectively. There is also cost associated with designing the system. The operating cost is covered by the section on energy efficiency and power consumption and we limited our coverage of design cost to the fact that custom DNN accelerators have a higher design cost than off-the-shelf CPUs and GPUs. We consider anything beyond this, e.g., the economics of the semiconductor business, including how to price platforms, is outside the scope of this book.

⁸ If obtained from simulation, it should be clarified whether it is from synthesis or post place-and-route and what library corner (e.g., process corner, supply voltage, temperature) was used.