Armin Sadighi, M.Sc.

Wissenschaftlicher Mitarbeiter  

Technische Universität München
Fakultät für Elektrotechnik und Informationstechnik
Lehrstuhl für Integrierte Systeme
Arcisstr. 21
80290 München

Tel.: +49.89.289.25294
Fax: +49.89.289.28323
Gebäude: N1 (Theresienstr. 90)
Raum: N2118
Email: armin.sadighi@tum.de

Angebotene Arbeiten

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Titel
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Learning Control for Predictable Latency and Low Energy

Learning Control for Predictable Latency and Low Energy

Beschreibung

Many modern computing systems must provide reliable latency with minimal energy. Two central challenges arise when allocating system resources to meet these conflicting goals: (1) complexityÐmodern hardware exposes diverse resources with complicated interactionsÐand (2) dynamicsÐ latency must be maintained despite unpredictable changes in operating environment or input. Machine learning accurately models the latency of complex, interacting resources, but does not address system dynamics; control theory adjusts to dynamic changes, but struggles with complex resource interaction. We therefore propose CALOREE, a resource manager that learns key control parameters to meet latency requirements with minimal energy in complex, dynamic environments. CALOREE breaks resource allocation into two sub-tasks: learning how interacting resources affect speedup, and controlling speedup to meet latency requirements with minimal energy. CALOREE deines a general control systemÐ whose parameters are customized by a learning frameworkÐ while maintaining control-theoretic formal guarantees that the latency goal will be met. We test CALOREE’s ability to deliver reliable latency on heterogeneous ARM big.LITTLE architectures in both single and multi-application scenarios. Compared to the best prior learning and control solutions, CALOREE reduces deadline misses by 60% and energy consumption by 13%.

Betreuer:

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Learning Control for Predictable Latency and Low Energy

Learning Control for Predictable Latency and Low Energy

Beschreibung

Many modern computing systems must provide reliable latency with minimal energy. Two central challenges arise when allocating system resources to meet these conflicting goals: (1) complexityÐmodern hardware exposes diverse resources with complicated interactionsÐand (2) dynamicsÐ latency must be maintained despite unpredictable changes in operating environment or input. Machine learning accurately models the latency of complex, interacting resources, but does not address system dynamics; control theory adjusts to dynamic changes, but struggles with complex resource interaction. We therefore propose CALOREE, a resource manager that learns key control parameters to meet latency requirements with minimal energy in complex, dynamic environments. CALOREE breaks resource allocation into two sub-tasks: learning how interacting resources affect speedup, and controlling speedup to meet latency requirements with minimal energy. CALOREE deines a general control systemÐ whose parameters are customized by a learning frameworkÐ while maintaining control-theoretic formal guarantees that the latency goal will be met. We test CALOREE’s ability to deliver reliable latency on heterogeneous ARM big.LITTLE architectures in both single and multi-application scenarios. Compared to the best prior learning and control solutions, CALOREE reduces deadline misses by 60% and energy consumption by 13%.

Betreuer:

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Multilayer Resource Controllers to Maximize Efficiency

Multilayer Resource Controllers to Maximize Efficiency

Beschreibung

Since computers increasingly execute in constrained environments, they are being equipped with controllers for resource management. However, the operation of modern computer systems is structured in multiple layers, such as the hardware, OS, and networking layers—each with its own resources. Managing such a system scalably and portably requires that we have a controller in each layer, and that the different controllers coordinate their operation. In addition, such controllers should not rely on heuristics, but be based on formal control theory. This paper presents a new approach to build coordinated multilayer formal controllers for computers. The approach uses Structured Singular Value (SSV) controllers from Robust Control Theory. Such controllers are especially suited for multilayer computer system control. Indeed, SSV controllers can read signals from other controllers to coordinate multilayer operation. In addition, they allow designers to specify the discrete values allowed in each input, and the desired bounds on output value deviations. Finally, they accept uncertainty guardbands, which incorporate the effects of interference between the controllers. We call this approach Yukta. To assess its effectiveness, we prototype it in an 8-core big.LITTLE board. We build a two-layer SSV controller, and show that it is very effective. Yukta reduces the E×D and the execution time of a set of applications by an average of 50% and 38%, respectively, over advanced heuristic-based coordinated controllers.

Betreuer:

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Multilayer Resource Controllers to Maximize Efficiency

Multilayer Resource Controllers to Maximize Efficiency

Beschreibung

Since computers increasingly execute in constrained environments, they are being equipped with controllers for resource management. However, the operation of modern computer systems is structured in multiple layers, such as the hardware, OS, and networking layers—each with its own resources. Managing such a system scalably and portably requires that we have a controller in each layer, and that the different controllers coordinate their operation. In addition, such controllers should not rely on heuristics, but be based on formal control theory. This paper presents a new approach to build coordinated multilayer formal controllers for computers. The approach uses Structured Singular Value (SSV) controllers from Robust Control Theory. Such controllers are especially suited for multilayer computer system control. Indeed, SSV controllers can read signals from other controllers to coordinate multilayer operation. In addition, they allow designers to specify the discrete values allowed in each input, and the desired bounds on output value deviations. Finally, they accept uncertainty guardbands, which incorporate the effects of interference between the controllers. We call this approach Yukta. To assess its effectiveness, we prototype it in an 8-core big.LITTLE board. We build a two-layer SSV controller, and show that it is very effective. Yukta reduces the E×D and the execution time of a set of applications by an average of 50% and 38%, respectively, over advanced heuristic-based coordinated controllers

Betreuer:

Laufende Arbeiten

Hauptseminare

The Evolution of Bitcoin Hardware

The Evolution of Bitcoin Hardware

Beschreibung

Since its deployment in 2009, Bitcoin has achieved remarkable success and spawned hundreds of other cryptocurrencies. This seminar topic traces the evolution of the hardware underlying the system, from early GPU-based homebrew machines to today’s datacenters powered by application-specific integrated circuits. These ASIC clouds provide a glimpse into planet-scale computing’s future.

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