  With increasing diversity in Deep Neural Network(DNN) models in terms of
layer shapes and sizes, the research community has been investigating
flexible/reconfigurable accelerator substrates. This line of research has
opened up two challenges. The first is to determine the appropriate amount of
flexibility within an accelerator array that that can trade-off the performance
benefits versus the area overheads of the reconfigurability. The second is
being able to determine the right configuration of the array for the current
DNN model and/or layer and reconfigure the accelerator at runtime. This work
introduces a new class of accelerators that we call Self Adaptive
Reconfigurable Array (SARA). SARA architectures comprise of both a
reconfigurable array and a hardware unit capable of determining an optimized
configuration for the array at runtime. We demonstrate an instance of SARA with
an accelerator we call SAGAR, which introduces a novel reconfigurable systolic
array that can be configured to work as a distributed collection of smaller
arrays of various sizes or as a single array with flexible aspect ratios. We
also develop a novel recommendation neural network called ADAPTNET which
recommends an array configuration and dataflow for the current layer
parameters. ADAPTNET runs on an integrated custom hardware ADAPTNETX that runs
ADAPTNET at runtime and reconfigures the array, making the entire accelerator
self-sufficient. SAGAR is capable of providing the same mapping flexibility as
a collection of 10244x4 arrays working as a distributed system while achieving
3.5x more power efficiency and 3.2x higher compute density Furthermore, the
runtime achieved on the recommended parameters from ADAPTNET is 99.93% of the
best achievable runtime.
