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Deep Neural Networks (DNN) have gained tremendous popularity over the last years for several computer vision tasks, including classification and object detection. Such techniques have been able to achieve human-level performance in many tasks and have produced results of unprecedented accuracy. As DNNs have intense computational requirements in the majority of applications, they utilize a cluster of computers or a cutting edge Graphical Processing Unit (GPU), often having excessive power consumption and generating a lot of heat. In many robotics applications the above requirements prove to be a challenge, as there is limited power on-board and heat dissipation is always a problem. In particular in underwater robotics with limited space, the above two requirements have been proven prohibitive. As first of this kind, this paper aims at analyzing and comparing the performance of several state-of-the-art DNNs on different platforms. With a focus on the underwater domain, the capabilities of the Jetson TX2 from NVIDIA and the Neural Compute Stick from Intel are of particular interest. Experiments on standard datasets show how different platforms are usable on an actual robotic system, providing insights on the current state-of-the-art embedded systems. Based on such results, we propose some guidelines in choosing the appropriate platform and network architecture for a robotic system.
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Simulation Support for Fast and Accurate Large-Scale GPGPU & Accelerator Workloads
In recent years deep neural networks (DNNs) have emerged as an important application domain driving the requirements for future systems. As DNNs get more sophisticated, their compute requirements and the datasets they are trained on continue to grow at a fast rate. For example, Gholami showed that compute in Transformer networks grew 750X over 2 years, while other work projects DNN compute and memory requirements to grow by 1.5X per year. Given their growing requirements and importance, heterogeneous systems often add machine learning (ML) specific features (e.g., TensorCores) to improve their efficiency. However, given ML’s voracious rate of growth and size, there is a growing challenge in performing early-system exploration based on sound simulation methodology. In this work we discuss our efforts to enhance gem5’s support to make these workloads practical to run while retaining accuracy.
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- Award ID(s):
- 2311889
- PAR ID:
- 10542853
- Publisher / Repository:
- 3rd Open-Source Computer Architecture Research Workshop (OSCAR)
- Date Published:
- Subject(s) / Keyword(s):
- simulation GPGPU ML gem5
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
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