DLAS: An Exploration and Assessment of the Deep Learning Acceleration Stack

• Main Authors: Gibson, Perry, Cano, José, Crowley, Elliot J, Storkey, Amos, O'Boyle, Michael
• Format: Journal Article
• Language: English
• Published: 15-11-2023
• Subjects: Computer Science - Computer Vision and Pattern Recognition; Computer Science - Learning; Computer Science - Performance
• DOI: 10.48550/arxiv.2311.08909

Deep Neural Networks (DNNs) are extremely computationally demanding, which
presents a large barrier to their deployment on resource-constrained devices.
Since such devices are where many emerging deep learning applications lie
(e.g., drones, vision-based medical technology), significant bodies of work
from both the machine learning and systems communities have attempted to
provide optimizations to accelerate DNNs. To help unify these two perspectives,
in this paper we combine machine learning and systems techniques within the
Deep Learning Acceleration Stack (DLAS), and demonstrate how these layers can
be tightly dependent on each other with an across-stack perturbation study. We
evaluate the impact on accuracy and inference time when varying different
parameters of DLAS across two datasets, seven popular DNN architectures, four
DNN compression techniques, three algorithmic primitives with sparse and dense
variants, untuned and auto-scheduled code generation, and four hardware
platforms. Our evaluation highlights how perturbations across DLAS parameters
can cause significant variation and across-stack interactions. The highest
level observation from our evaluation is that the model size, accuracy, and
inference time are not guaranteed to be correlated. Overall we make 13 key
observations, including that speedups provided by compression techniques are
very hardware dependent, and that compiler auto-tuning can significantly alter
what the best algorithm to use for a given configuration is. With DLAS, we aim
to provide a reference framework to aid machine learning and systems
practitioners in reasoning about the context in which their respective DNN
acceleration solutions exist in. With our evaluation strongly motivating the
need for co-design, we believe that DLAS can be a valuable concept for
exploring the next generation of co-designed accelerated deep learning
solutions.