A Methodology to Design Pipelined Simulated Annealing Kernel Accelerators on Space-Borne Field-Programmable Gate Arrays

Increased levels of science objectives expected from spacecraft systems necessitate the ability to carry out fast on-board autonomous mission planning and scheduling. Heterogeneous radiation-hardened Field Programmable Gate Arrays (FPGAs) with embedded multiplier and memory modules are well suited to support the acceleration of scheduling algorithms. A methodology to design circuits specifically to accelerate Simulated Annealing Kernels (SAKs) in event scheduling algorithms is shown. The main contribution of this thesis is the low complexity scoring calculation used for the heuristic mapping algorithm used to balance resource allocation across a coarse-grained pipelined data-path. The methodology was exercised over various kernels with different cost functions and problem sizes. These test cases were benchedmarked for execution time, resource usage, power, and energy on a Xilinx Virtex 4 LX QR 200 FPGA and a BAE RAD 750 microprocessor

Design Space Exploration and Resource Management of Multi/Many-Core Systems

The increasing demand of processing a higher number of applications and related data on computing platforms has resulted in reliance on multi-/many-core chips as they facilitate parallel processing. However, there is a desire for these platforms to be energy-efficient and reliable, and they need to perform secure computations for the interest of the whole community. This book provides perspectives on the aforementioned aspects from leading researchers in terms of state-of-the-art contributions and upcoming trends

A comprehensive approach to DRAM power management

This paper describes a comprehensive approach for using the memory controller to improve DRAM energy efficiency and manage DRAM power. We make three contributions: (1) we describe a simple power-down policy for exploiting low power modes of modern DRAMs; (2) we show how the idea of adaptive history-based memory schedulers can be naturally extended to manage power and energy; and (3) for situations in which additional DRAM power reduction is needed, we present a throttling approach that arbitrarily reduces DRAM activity by delaying the issuance of memory commands. Using detailed microarchitectural simulators of the IBM Power5+ and a DDR2-533 SDRAM, we show that our first two techniques combine to increase DRAM energy efficiency by an average of 18.2%, 21.7%, 46.1%, and 37.1 % for the Stream, NAS, SPEC2006fp, and commercial benchmarks, respectively. We also show that our throttling approach provides performance that is within 4.4 % of an idealized oracular approach.

Modeling and optimization of high-performance many-core systems for energy-efficient and reliable computing

Thesis (Ph.D.)--Boston UniversityMany-core systems, ranging from small-scale many-core processors to large-scale high performance computing (HPC) data centers, have become the main trend in computing system design owing to their potential to deliver higher throughput per watt. However, power densities and temperatures increase following the growth in the performance capacity, and bring major challenges in energy efficiency, cooling costs, and reliability. These challenges require a joint assessment of performance, power, and temperature tradeoffs as well as the design of runtime optimization techniques that monitor and manage the interplay among them. This thesis proposes novel modeling and runtime management techniques that evaluate and optimize the performance, energy, and reliability of many-core systems. We first address the energy and thermal challenges in 3D-stacked many-core processors. 3D processors with stacked DRAM have the potential to dramatically improve performance owing to lower memory access latency and higher bandwidth. However, the performance increase may cause 3D systems to exceed the power budgets or create thermal hot spots. In order to provide an accurate analysis and enable the design of efficient management policies, this thesis introduces a simulation framework to jointly analyze performance, power, and temperature for 3D systems. We then propose a runtime optimization policy that maximizes the system performance by characterizing the application behavior and predicting the operating points that satisfy the power and thermal constraints. Our policy reduces the energy-delay product (EDP) by up to 61.9% compared to existing strategies. Performance, cooling energy, and reliability are also critical aspects in HPC data centers. In addition to causing reliability degradation, high temperatures increase the required cooling energy. Communication cost, on the other hand, has a significant impact on system performance in HPC data centers. This thesis proposes a topology-aware technique that maximizes system reliability by selecting between workload clustering and balancing. Our policy improves the system reliability by up to 123.3% compared to existing temperature balancing approaches. We also introduce a job allocation methodology to simultaneously optimize the communication cost and the cooling energy in a data center. Our policy reduces the cooling cost by 40% compared to cooling-aware and performance-aware policies, while achieving comparable performance to performance-aware policy

Power-efficient data management for dynamic applications

In recent years, the semiconductor industry has turned its focus towards heterogeneous multi-processor platforms. They are an economically viable solution for coping with the growing setup and manufacturing cost of silicon systems. Furthermore, their inherent flexibility also perfectly supports the emerging market of interactive, mobile data and content services. The platform's performance and energy depend largely on how well the data-dominated services are mapped on the memory subsystem. A crucial aspect thereby is how efficient data is transferred between the different memory layers. Several compilation techniques have been developed to optimally use the available bandwidth. Unfortunately, they do not take the interaction between multiple threads running on the different processors into account, only locally optimize the bandwidth nor deal with the dynamic behavior of these applications. The contributions of this chapter are to outline the main limitations of current techniques and to introduce an approach for dealing with the dynamic multi-threaded of our application domain

SOMA A Tool for Synthesizing and Optimizing Memory Accesses in ASICs

Arbitrary memory dependencies and variable latency memory systems are major obstacles to the synthesis of large-scale ASIC systems in high-level synthesis. This paper presents SOMA, a synthesis framework for constructing Memory Access Network (MAN) architectures that inherently enforce memory consistency in the presence of dynamic memory access dependencies. A fundamental bottleneck in any such network is arbitrating between concurrent accesses to a shared memory resource. To alleviate this bottleneck, SOMA uses an application-specific concurrency analysis technique to predict the dynamic memory parallelism profile of the application. This is then used to customize the MAN architecture. Depending on the parallelism profile, the MAN may be optimized for latency, throughput or both. The optimized MAN is automatically synthesized into gate-level structural Verilog using a flexible library of network building blocks. SOMA has been successfully integrated into an automated C-to-hardware synthesis flow, which generates standard cell circuits from unrestricted ANSI-C programs. Post-layout experiments demonstrate that application specific MAN construction significantly improves power and performance