BrezeFlow: Unified Debugger for Android CPU Power Governors and Schedulers on Edge Devices

Hoffman, A. A. J.; Pathania, Anuj; Kindt, Philipp H.; Chakraborty, Samarjit; Mitra, Tulika

doi:10.1109/dac18072.2020.9218542

Cited by 2 publications

(1 citation statement)

References 15 publications

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“…However, the tool has only a limited usage. Breazeflow [18] is a tool that identifies wrong decisions taken by frequency governor and scheduler and thus helps kernel developers to efficiently perform thermal management.…”

Section: Related Workmentioning

confidence: 99%

SLePaaS: An Embedded Platform-as-a-Service Facilitating Research on Thermal Management of Embedded Platforms

2022

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In this paper, we present to the embedded research community an Embedded Platform as a Service facility named SLePaaS that allows researchers remote access to experimental hardware and a collection of online tools that would facilitate research on thermal management of embedded systems. SLePaaS serves a dual purpose -provides access to actual hardware for experiments on one hand and reduces experimental cost and time on the other. With this facility, researchers neither have to rely on simulation models nor purchase or customize any experimental platform for a specific problem. In addition, the platform provides tool support, specifically for researchers working in problems related to thermal management. Presently, three tools are provided by the SLePaaS platform. The first tool helps users obtain performance, power and thermal profile of applications running on different hardware platforms. The second is a thermal value predictor tool that aids task schedulers in allocating tasks to processors based on the thermal profile of applications. The third tool is a platform predictor that helps users to decide the best platform to run an OpenCL application given a target optimization objective. All the tools have been validated for their performance and accuracy against standard benchmark applications. A prototype implementation of SLePaaS along with the three tools is available for use.

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Section: Related Workmentioning

confidence: 99%

SLePaaS: An Embedded Platform-as-a-Service Facilitating Research on Thermal Management of Embedded Platforms

2022

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ARM-CO-UP: ARM CO operative U tilization of P rocessors

Aghapour,

Sapra,

Pimentel

et al. 2024

ACM Trans. Des. Autom. Electron. Syst.

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HMPSoCs combine different processors on a single chip. They enable powerful embedded devices, which increasingly perform ML inference tasks at the edge. State-of-the-art HMPSoCs can perform on-chip embedded inference using different processors, such as CPUs, GPUs, and NPUs. HMPSoCs can potentially overcome the limitation of low single-processor CNN inference performance and efficiency by cooperative use of multiple processors. However, standard inference frameworks for edge devices typically utilize only a single processor. We present the ARM-CO-UP framework built on the ARM-CL library. The ARM-CO-UP framework supports two modes of operation – Pipeline and Switch. It optimizes inference throughput using pipelined execution of network partitions for consecutive input frames in the Pipeline mode. It improves inference latency through layer-switched inference for a single input frame in the Switch mode. Furthermore, it supports layer-wise CPU/GPU DVFS in both modes for improving power efficiency and energy consumption. ARM-CO-UP is a comprehensive framework for multi-processor CNN inference that automates CNN partitioning and mapping, pipeline synchronization, processor type switching, layer-wise DVFS , and closed-source NPU integration.

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BrezeFlow: Unified Debugger for Android CPU Power Governors and Schedulers on Edge Devices

Cited by 2 publications

References 15 publications

SLePaaS: An Embedded Platform-as-a-Service Facilitating Research on Thermal Management of Embedded Platforms

SLePaaS: An Embedded Platform-as-a-Service Facilitating Research on Thermal Management of Embedded Platforms

ARM-CO-UP: ARM CO operative U tilization of P rocessors

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