Low Power Processor Architectures and Contemporary Techniques for Power Optimization – A Review

The technological evolution has increased the number of transistors for a given die area significantly and increased the switching speed from few MHz to GHz range. Such inversely proportional decline in size and boost in performance consequently demands shrinking of supply voltage and effective power dissipation in chips with millions of transistors. This has triggered substantial amount of research in power reduction techniques into almost every aspect of the chip and particularly the processor cores contained in the chip. This paper presents an overview of techniques for achieving the power efficiency mainly at the processor core level but also visits related domains such as buses and memories. There are various processor parameters and features such as supply voltage, clock frequency, cache and pipelining which can be optimized to reduce the power consumption of the processor. This paper discusses various ways in which these parameters can be optimized. Also, emerging power efficient processor architectures are overviewed and research activities are discussed which should help reader identify how these factors in a processor contribute to power consumption. Some of these concepts have been already established whereas others are still active research areas. © 2009 ACADEMY PUBLISHER

A Scalable Correlator Architecture Based on Modular FPGA Hardware, Reuseable Gateware, and Data Packetization

A new generation of radio telescopes is achieving unprecedented levels of sensitivity and resolution, as well as increased agility and field-of-view, by employing high-performance digital signal processing hardware to phase and correlate large numbers of antennas. The computational demands of these imaging systems scale in proportion to BMN^2, where B is the signal bandwidth, M is the number of independent beams, and N is the number of antennas. The specifications of many new arrays lead to demands in excess of tens of PetaOps per second. To meet this challenge, we have developed a general purpose correlator architecture using standard 10-Gbit Ethernet switches to pass data between flexible hardware modules containing Field Programmable Gate Array (FPGA) chips. These chips are programmed using open-source signal processing libraries we have developed to be flexible, scalable, and chip-independent. This work reduces the time and cost of implementing a wide range of signal processing systems, with correlators foremost among them,and facilitates upgrading to new generations of processing technology. We present several correlator deployments, including a 16-antenna, 200-MHz bandwidth, 4-bit, full Stokes parameter application deployed on the Precision Array for Probing the Epoch of Reionization.Comment: Accepted to Publications of the Astronomy Society of the Pacific. 31 pages. v2: corrected typo, v3: corrected Fig. 1

Pipeline AER arbitration with event aging

We present a simple circuit to handle communication between cells of neuromorphic arrays. It allows cells to operate continuously without waiting for acknowledgement signals back from the AER (Address Event Representation) arbitration circuitry. The module also implements aging of cell petitions i.e., old petitions to access to the AER bus are automatically discarded to give priority to the more recent ones and alleviate the bus congestion. The new arbitration scheme has been implemented and tested. A particular application scenario with an image sensor with spiking pixels that sense light continuously is explained. The sensing errors per event due to discontinued pixel operation can be minimized a factor 8.1. Experimental data obtained with real visual scenes are provided.Universidad de Cádiz PR2016-072Ministerio de Economía y Competitividad TEC2015-66878- C3-1-RJunta de Andalucía TIC 2012-2338Office of Naval Research (USA) N00014141035

Automatic rapid prototyping of semi-custom VLSI circuits using FPGAs

Journal ArticleWe describe a technique for translating semi-custom VLSI circuits automatically, integrating two design environments, into field programmable gate arrays (FPGAs) for rapid and inexpensive prototyping. The VLSI circuits are designed using a cell-matrix based environment that produces chips with density comparable to full custom VLSI design. These circuits are translated automatically into FPGAs for testing and system development. A four-bit pipelined array multiplier is used as an example of this translation. The multiplier is implemented in CMOS in both synchronous and asynchronous pipelined versions, and translated into Actel FPGAs both automatically, and by hand for comparison. The six test chips were all found to be fully functional, and the translation efficiency in terms of chip speed and area is shown. This result demonstrates the potential of this approach to system development

OpenCL Actors - Adding Data Parallelism to Actor-based Programming with CAF

The actor model of computation has been designed for a seamless support of concurrency and distribution. However, it remains unspecific about data parallel program flows, while available processing power of modern many core hardware such as graphics processing units (GPUs) or coprocessors increases the relevance of data parallelism for general-purpose computation. In this work, we introduce OpenCL-enabled actors to the C++ Actor Framework (CAF). This offers a high level interface for accessing any OpenCL device without leaving the actor paradigm. The new type of actor is integrated into the runtime environment of CAF and gives rise to transparent message passing in distributed systems on heterogeneous hardware. Following the actor logic in CAF, OpenCL kernels can be composed while encapsulated in C++ actors, hence operate in a multi-stage fashion on data resident at the GPU. Developers are thus enabled to build complex data parallel programs from primitives without leaving the actor paradigm, nor sacrificing performance. Our evaluations on commodity GPUs, an Nvidia TESLA, and an Intel PHI reveal the expected linear scaling behavior when offloading larger workloads. For sub-second duties, the efficiency of offloading was found to largely differ between devices. Moreover, our findings indicate a negligible overhead over programming with the native OpenCL API.Comment: 28 page

A polymorphic hardware platform

In the domain of spatial computing, it appears that platforms based on either reconfigurable datapath units or on hybrid microprocessor/logic cell organizations are in the ascendancy as they appear to offer the most efficient means of providing resources across the greatest range of hardware designs. This paper encompasses an initial exploration of an alternative organization. It looks at the effect of using a very fine-grained approach based on a largely undifferentiated logic cell that can be configured to operate as a state element, logic or interconnect - or combinations of all three. A vertical layout style hides the overheads imposed by reconfigurability to an extent where very fine-grained organizations become a viable option. It is demonstrated that the technique can be used to develop building blocks for both synchronous and asynchronous circuits, supporting the development of hybrid architectures such as globally asynchronous, locally synchronous

Transformations of High-Level Synthesis Codes for High-Performance Computing

Specialized hardware architectures promise a major step in performance and energy efficiency over the traditional load/store devices currently employed in large scale computing systems. The adoption of high-level synthesis (HLS) from languages such as C/C++ and OpenCL has greatly increased programmer productivity when designing for such platforms. While this has enabled a wider audience to target specialized hardware, the optimization principles known from traditional software design are no longer sufficient to implement high-performance codes. Fast and efficient codes for reconfigurable platforms are thus still challenging to design. To alleviate this, we present a set of optimizing transformations for HLS, targeting scalable and efficient architectures for high-performance computing (HPC) applications. Our work provides a toolbox for developers, where we systematically identify classes of transformations, the characteristics of their effect on the HLS code and the resulting hardware (e.g., increases data reuse or resource consumption), and the objectives that each transformation can target (e.g., resolve interface contention, or increase parallelism). We show how these can be used to efficiently exploit pipelining, on-chip distributed fast memory, and on-chip streaming dataflow, allowing for massively parallel architectures. To quantify the effect of our transformations, we use them to optimize a set of throughput-oriented FPGA kernels, demonstrating that our enhancements are sufficient to scale up parallelism within the hardware constraints. With the transformations covered, we hope to establish a common framework for performance engineers, compiler developers, and hardware developers, to tap into the performance potential offered by specialized hardware architectures using HLS