05141 Abstracts Collection -- Power-aware Computing Systems

From 03.04.05 to 08.04.05, the Dagstuhl Seminar 05141 ``Power-aware Computing Systems\u27\u27 was held in the International Conference and Research Center (IBFI), Schloss Dagstuhl. During the seminar, several participants presented their current research, and ongoing work and discussed open problems. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are collected in this paper. The first section describes the seminar topics and goals. Links to extended abstracts or full papers are provided, if available

Extending the reach of microprocessors : column and curious caching

Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1999.Includes bibliographical references (p. 162-167).by Derek T. Chiou.Ph.D

Power models, energy models and libraries for energy-efficient concurrent data structures and algorithms

EXCESS deliverable D2.3. More information at http://www.excess-project.eu/This deliverable reports the results of the power models, energy models and librariesfor energy-efficient concurrent data structures and algorithms as available by projectmonth 30 of Work Package 2 (WP2). It reports i) the latest results of Task 2.2-2.4 onproviding programming abstractions and libraries for developing energy-efficient datastructures and algorithms and ii) the improved results of Task 2.1 on investigating andmodeling the trade-off between energy and performance of concurrent data structuresand algorithms. The work has been conducted on two main EXCESS platforms: Intelplatforms with recent Intel multicore CPUs and Movidius Myriad platforms

3rd Many-core Applications Research Community (MARC) Symposium. (KIT Scientific Reports ; 7598)

This manuscript includes recent scientific work regarding the Intel Single Chip Cloud computer and describes approaches for novel approaches for programming and run-time organization

Speculative Techniques for Memory Hierarchy Management

The “Memory Wall” [1], is the gap in performance between the processor and the main memory. Over the last 30 years computer architects have added multiple levels of cache to fill this gap, cache levels that are closer to the processors are smaller and faster. On the other hand, the levels that are far from the processors are bigger and slower. However the processors are still exposed to the latency of DRAM on misses. Therefore, speculative memory management techniques such as prefetching are used in modern microprocessors to bridge this gap in performance. First, we propose Synchronization-aware Hardware Prefetching for Chip Multiprocessors, a novel hardware data prefetching scheme designed for prefetching shared-memory, multi- threaded workloads. This is the first work we are aware of to characterize the causes of poor prefetching performance in shared- memory multi-threaded applications. These are the inability to prefetch beyond synchronization points and tendency to prefetch shared data before it has been written. SB-Fetch, a low-complexity, low-overhead prefetcher design that addresses both issues. Second, we propose a new prefetching algorithm, Set-Level Adaptive Prefetching for Com- pressed Caches (SLAP-CC), which seeks to address this problem by varying the prefetching aggressiveness based on how much effective capacity is available in each set. The ontribu- tions of this work is characterize the increase and per-set variability of cache efficiency which typical cache compression schemes create, and propose a new prefetching scheme, SLAP-CC, designed to leverage this cache efficiency variability. Third, we propose a new a scheduling mechanism that predicts the hard- to-prefetch loads at issue time and preemptively schedule them for execution as soon as they are ready, to allow the cache hierarchy to start the mishandling mechanism sooner. Such scheduling mechanism reduces the miss penalty on the dependent instructions after a hard-to-prefetch loads

Heap Data Allocation to Scratch-Pad Memory in Embedded Systems

This thesis presents the first-ever compile-time method for allocating a portion of a program's dynamic data to scratch-pad memory. A scratch-pad is a fast directly addressed compiler-managed SRAM memory that replaces the hardware-managed cache. It is motivated by its better real-time guarantees vs cache and by its significantly lower overheads in access time, energy consumption, area and overall runtime. Dynamic data refers to all objects allocated at run-time in a program, as opposed to static data objects which are allocated at compile-time. Existing compiler methods for allocating data to scratch-pad are able to place only code, global and stack data (static data) in scratch-pad memory; heap and recursive-function objects(dynamic data) are allocated entirely in DRAM, resulting in poor performance for these dynamic data types. Runtime methods based on software caching can place data in scratch-pad, but because of their high overheads from software address translation, they have not been successful, especially for dynamic data. In this thesis we present a dynamic yet compiler-directed allocation method for dynamic data that for the first time, (i) is able to place a portion of the dynamic data in scratch-pad; (ii) has no software-caching tags; (iii) requires no run-time per-access extra address translation; and (iv) is able to move dynamic data back and forth between scratch-pad and DRAM to better track the program's locality characteristics. With our method, code, global, stack and heap variables can share the same scratch-pad. When compared to placing all dynamic data variables in DRAM and only static data in scratch-pad, our results show that our method reduces the average runtime of our benchmarks by 22.3%, and the average power consumption by 26.7%, for the same size of scratch-pad fixed at 5% of total data size. Significant savings in runtime and energy across a large number of benchmarks were also observed when compared against cache memory organizations, showing our method's success under constrained SRAM sizes when dealing with dynamic data. Lastly, our method is able to minimize the profile dependence issues which plague all similar allocation methods through careful analysis of static and dynamic profile information

Running deep learning applications on resource constrained devices

The high accuracy of Deep Neural Networks (DNN) come at the expense of high computational cost and memory requirements. During inference, the data is often collected on the edge device which are resource-constrained. The existing solutions for edge deployment include i) executing the entire DNN on the edge (EDGE-ONLY), ii) sending the input from edge to cloud where the DNN is processed (CLOUD-ONLY), and iii) splitting the DNN to execute partially on the edge and partially on the cloud (SPLIT). The choice of deployment between EDGE-ONLY, CLOUD-ONLY and SPLIT is determined by several operating constraints such as device resources and network speed, and application constraints such as latency and accuracy. The EDGE-ONLY approach requires compact DNN with low compute and memory requirements. Thus, the emerging class of DNNs employ low-rank convolutions (LRCONVs) which reduce one or more dimensions compared to the spatial convolutions (CONV). Prior research in hardware accelerators has largely focused on CONVs. The LRCONVs such as depthwise and pointwise convolutions exhibit lower arithmetic intensity and lower data reuse. Thus, LRCONVs result in low hardware utilization and high latency. In our first work, we systematically explore the design space of Cross-layer dataflows to exploit data reuse across layers for emerging DNNs in EDGE-ONLY scenarios. We develop novel fine-grain cross-layer dataflows for LRCONVs that support partial loop dimension completion. Our tool, X-Layer decouples the nested loops in a pipeline and combines them to create a common outer dataflow and several inner dataflows. The CLOUD-ONLY approach can suffer from high latency due to the high transmission cost of large input data from the edge to the cloud. This could be a problem, especially for latency-critical applications. Thankfully, the SPLIT approach reduces latency compared to the CLOUD-ONLY approach. However, existing solutions only split the DNN in floating-point precision. Executing floating-point precision on the edge device can occupy large memory and reduce the potential options for SPLIT solutions. In our second work, we expand and explore the search space of SPLIT solutions by jointly applying mixed-precision post-training quantization and DNN graph split. Our work, Auto-Split finds a balance in the trade-off among the model accuracy, edge device capacity, transmission cost, and the overall latency

WCET-aware prefetching of unlocked instruction caches: a technique for reconciling real-time guarantees and energy efficiency

Tese (doutorado) - Universidade Federal de Santa Catarina, Centro Tecnológico, Programa de Pós-Graduação em Engenharia de Automação e Sistemas, Florianópolis, 2015.A computação embarcada requer crescente vazão sob baixa potência. Ela requer um aumento de eficiência energética quando se executam programas de crescente complexidade. Muitos sistemas embarcados são também sistemas de tempo real, cuja correção temporal precisa ser garantida através de análise de escalonabilidade, a qual costuma assumir que o WCET de uma tarefa é conhecido em tempo de projeto. Como resultado da crescente complexidade do software, uma quantidade significativa de energia é gasta ao se prover instruções através da hierarquia de memória. Como a cache de instruções consome cerca de 40% da energia gasta em um processador embarcado e afeta a energia consumida em memória principal, ela se torna um relevante alvo para otimização. Entretanto, como ela afeta substancialmente o WCET, o comportamento da cache precisa ser restrito via  cache locking ou previsto via análise de WCET. Para obter eficiência energética sob restrições de tempo real, é preciso estender a consciência que o compilador tem da plataforma de hardware. Entretanto, compiladores para tempo real ignoram a energia, embora determinem rapidamente limites superiores para o WCET, enquanto compiladores para sistemas embarcados estimem com precisão a energia, mas gastem muito tempo em  profiling . Por isso, esta tese propõe um método unificado para estimar a energia gasta em memória, o qual é baseado em Interpretação Abstrata, exatamente o mesmo substrato matemático usado para a análise de WCET em caches. As estimativas mostram derivadas que são tão precisas quanto as obtidas via  profiling , mas são computadas 1000 vezes mais rápido, sendo apropriadas para induzir otimização de código através de melhoria iterativa. Como  cache locking troca eficiência energética por previsibilidade, esta tese propõe uma nova otimização de código, baseada em pré-carga por software, a qual reduz a taxa de faltas de caches de instruções e, provadamente, não aumenta o WCET. A otimização proposta é comparada com o estado-da-arte em  cache locking parcial para 37 programas do  Malardalen WCET benchmark para 36 configurações de cache e duas tecnologias distintas (2664 casos de uso). Em média, para obter uma melhoria de 68% no WCET,  cache locking parcial requer 8% mais energia. Por outro lado, a pré-carga por software diminui o consumo de energia em 11% enquanto melhora em 15% o WCET, reconciliando assim eficiência energética e garantias de tempo real.Abstract : Embedded computing requires increasing throughput at low power budgets. It asks for growing energy efficiency when executing programs of rising complexity. Many embedded systems are also real-time systems, whose temporal correctness is asserted through schedulability analysis, which often assumes that the WCET of each task is known at design-time. As a result of the growing software complexity, a significant amount of energy is spent in supplying instructions through the memory hierarchy. Since an instruction cache consumes around 40% of an embedded processor s energy and affects the energy spent in main memory, it becomes a relevant optimization target. However, since it largely impacts the WCET, cache behavior must be either constrained via cache locking or predicted by WCET analysis. To achieve energy efficiency under real-time constraints, a compiler must have extended awareness of the hardware platform. However, real-time compilers ignore energy, although they quickly determine bounds for WCET, whereas embedded compilers accurately estimate energy but require time-consuming profiling. That is why this thesis proposes a unifying method to estimate memory energy consumption that is based on Abstract Interpretation, the very same mathematical framework employed for the WCET analysis of caches. The estimates exhibit derivatives that are as accurate as those obtained by profiling, but are computed 1000 times faster, being suitable for driving code optimization through iterative improvement. Since cache locking gives up energy efficiency for predictability, this thesis proposes a novel code optimization, based on software prefetching, which reduces miss rate of unlocked instruction caches and, provenly, does not increase the WCET. The proposed optimization is compared with a state-of-the-art partial cache locking technique for the 37 programs of the Malardalen WCET benchmarks under 36 cache configurations and two distinct target technologies (2664 use cases). On average, to achieve an improvement of 68% in the WCET, partial cache locking required 8% more energy. On the other hand, software prefetching decreased the energy consumption by 11% while leading to an improvement of 15% in the WCET, thereby reconciling energy efficiency and real-time guarantees