Sub-Project 3: Energy-centric machine learning-circuit co-design This sub-project focuses on the algorithm-circuit interaction, through the
investigation of a novel class
of deep neural networks that will be designed and trained by including power
consumption as explicit
metric/cost function, as opposed to conventional machine
learning methods focusing on pure
accuracy [HVD2015]. Also, a
novel class of ultra-efficient
deep learning accelerators based on
the DDPM modulation (Fig. D12) will be investigated. In this sub-project,
we investigate systematic energy-aware model design
and training schemes,
introducing the energy cost within the training objective of the deep learning
model. Being circuit/architecture parameters within the
network optimization loop, this creates an interdependence
and ultimately a synergy that is of
particular interest for this sub-project.
At the same time, low-activity SRAM memories will be explored and
demonstrated. Machine learning circuit techniques will be explored that smartly allocate energy between training and
sense making, adding run-time criteria for early termination of the
computation, without incurring further
unnecessary energy cost while
accuracy is plateauing. The
developed energy-centric machine learning algorithm-circuit
co-design will
be validated in terms of accuracy
and energy in applications for
processing images at the resolution from 1,000x1,000 to 80x80 to assess the scalability of the proposed techniques. The
resulting models will be validated and integrated in the final silicon prototype first in a controlled
environment,and then in a real-world setting. Benchmarks provided by our project partners (see
letters of support from agencies) will be used to this purpose, covering human and object recognition, in addition to the popular AlexNet
benchmark (Table IV). |