A Formalism of DNN Accelerator Flexibility

The high efficiency of domain-specific hardware accelerators for machine learning (ML) has come from

specialization, with the trade-off of less configurability/ flexibility. There is growing interest in developing

flexible ML accelerators to make them future-proof to the rapid evolution of Deep Neural Networks (DNNs).

However, the notion of accelerator flexibility has always been used in an informal manner, restricting computer

architects from conducting systematic apples-to-apples design-space exploration (DSE) across trillions of

choices. In this work, we formally define accelerator flexibility and show how it can be integrated for DSE.

Specifically, we capture DNN accelerator flexibility across four axes: tiling, ordering, parallelization, and

array shape. We categorize existing accelerators into 16 classes based on their axes of flexibility support,

and define a precise quantification of the degree of flexibility of an accelerator across each axis. We leverage

these to develop a novel flexibility-aware DSE framework. We demonstrate how this can be used to perform

first-of-their-kind evaluations, including an isolation study to identify the individual impact of the flexibility

axes. We demonstrate that adding flexibility features to a hypothetical DNN accelerator designed in 2014

improves runtime on future (i.e., present-day) DNNs by 11.8× geomean.


Sheng-Chun Kao (Georgia Institute of Technology)
Hyoukjun Kwon (Georgia Institute of Technology)
Tushar Krishna (Georgia Institute of Technology)

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