You Only Search Once: A Fast Automation Framework for Single-Stage DNN/Accelerator Co-design
Weiwei Chen (1,2), Ying Wang (1), Shuang Yang (1,2), Chen Liu (1), Lei, Zhang (1) ((1) Institute of Computer Technology, Chinese Academy of Sciences,, Beijing, China, (2) University of Chinese Academy of Sciences, Beijing,, China)

TL;DR
YOSO is a single-stage automation framework that efficiently co-designs DNNs and accelerators, optimizing for accuracy, energy, and latency simultaneously, outperforming traditional two-stage methods.
Contribution
The paper introduces YOSO, a novel single-stage framework that jointly optimizes DNN and hardware design for better QoR and performance.
Findings
Achieves 1.42x~2.29x energy reduction at same precision
Achieves 1.79x~3.07x latency reduction
Outperforms two-stage co-design methods on baseline systolic array and Cifar10
Abstract
DNN/Accelerator co-design has shown great potential in improving QoR and performance. Typical approaches separate the design flow into two-stage: (1) designing an application-specific DNN model with high accuracy; (2) building an accelerator considering the DNN specific characteristics. However, it may fail in promising the highest composite score which combines the goals of accuracy and other hardware-related constraints (e.g., latency, energy efficiency) when building a specific neural-network-based system. In this work, we present a single-stage automated framework, YOSO, aiming to generate the optimal solution of software-and-hardware that flexibly balances between the goal of accuracy, power, and QoS. Compared with the two-stage method on the baseline systolic array accelerator and Cifar10 dataset, we achieve 1.42x~2.29x energy or 1.79x~3.07x latency reduction at the same level of…
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Taxonomy
TopicsAdvanced Neural Network Applications · Advanced Memory and Neural Computing · Ferroelectric and Negative Capacitance Devices
