DaCapo: Accelerating Continuous Learning in Autonomous Systems for Video
  Analytics
Yoonsung Kim Changhun Oh Jinwoo Hwang Wonung Kim Seongryong Oh Yubin Lee Hardik Sharma Amir Yazdanbakhsh Jongse Park
Deep neural network (DNN) video analytics is crucial for autonomous systems
such as self-driving vehicles, unmanned aerial vehicles (UAVs), and security
robots. However, real-world deployment faces challenges due to their limited
computational resources and battery power. To tackle these challenges,
continuous learning exploits a lightweight "student" model at deployment
(inference), leverages a larger "teacher" model for labeling sampled data
(labeling), and continuously retrains the student model to adapt to changing
scenarios (retraining). This paper highlights the limitations in
state-of-the-art continuous learning systems: (1) they focus on computations
for retraining, while overlooking the compute needs for inference and labeling,
(2) they rely on power-hungry GPUs, unsuitable for battery-operated autonomous
systems, and (3) they are located on a remote centralized server, intended for
multi-tenant scenarios, again unsuitable for autonomous systems due to privacy,
network availability, and latency concerns. We propose a hardware-algorithm
co-designed solution for continuous learning, DaCapo, that enables autonomous
systems to perform concurrent executions of inference, labeling, and training
in a performant and energy-efficient manner. DaCapo comprises (1) a
spatially-partitionable and precision-flexible accelerator enabling parallel
execution of kernels on sub-accelerators at their respective precisions, and
(2) a spatiotemporal resource allocation algorithm that strategically navigates
the resource-accuracy tradeoff space, facilitating optimal decisions for
resource allocation to achieve maximal accuracy. Our evaluation shows that
DaCapo achieves 6.5% and 5.5% higher accuracy than a state-of-the-art GPU-based
continuous learning systems, Ekya and EOMU, respectively, while consuming 254x
less power.