A Scalable Unsegmented Multiport Memory for FPGA-Based Systems

On-chip multiport memory cores are crucial primitives for many modern high-performance reconfigurable architectures and multicore systems. Previous approaches for scaling memory cores come at the cost of operating frequency, communication overhead, and logic resources without increasing the storage capacity of the memory. In this paper, we present two approaches for designing multiport memory cores that are suitable for reconfigurable accelerators with substantial on-chip memory or complex communication. Our design approaches tackle these challenges by banking RAM blocks and utilizing interconnect networks which allows scaling without sacrificing logic resources. With banking, memory congestion is unavoidable and we evaluate our multiport memory cores under different memory access patterns to gain insights about different design trade-offs. We demonstrate our implementation with up to 256 memory ports using a Xilinx Virtex-7 FPGA. Our experimental results report high throughput memories with resource usage that scales with the number of ports

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 pipelined configurable gate array for embedded processors

In recent years the challenge of high performance, low power retargettable embedded system has been faced with different technological and architectural solutions. In this paper we present a new configurable unit explicitly designed to imple-ment additional reconfigurable pipelined datapaths, suitable for the design of reconfigurable processors. A VLIW recon-figurable processor has been implemented on silicon in a standard 0.18 µm CMOS technology to prove the effective-ness of the proposed unit. Testing on a signal processing algorithms benchmark showed speedups from 4.3x to 13.5x and energy consumption reduction up to 92%

POWER-AWARE TECHNOLOGY MAPPING AND ROUTING FOR DUAL-VT FPGAS

Master'sMASTER OF ENGINEERIN

Just In Time Assembly (JITA) - A Run Time Interpretation Approach for Achieving Productivity of Creating Custom Accelerators in FPGAs

The reconfigurable computing community has yet to be successful in allowing programmers to access FPGAs through traditional software development flows. Existing barriers that prevent programmers from using FPGAs include: 1) knowledge of hardware programming models, 2) the need to work within the vendor specific CAD tools and hardware synthesis. This thesis presents a series of published papers that explore different aspects of a new approach being developed to remove the barriers and enable programmers to compile accelerators on next generation reconfigurable manycore architectures. The approach is entitled Just In Time Assembly (JITA) of hardware accelerators. The approach has been defined to allow hardware accelerators to be built and run through software compilation and run time interpretation outside of CAD tools and without requiring each new accelerator to be synthesized. The approach advocates the use of libraries of pre-synthesized components that can be referenced through symbolic links in a similar fashion to dynamically linked software libraries. Synthesis still must occur but is moved out of the application programmers software flow and into the initial coding process that occurs when programming patterns that define a Domain Specific Language (DSL) are first coded. Programmers see no difference between creating software or hardware functionality when using the DSL. A new run time interpreter is introduced to assemble the individual pre-synthesized hardware accelerators that comprise the accelerator functionality within a configurable tile array of partially reconfigurable slots at run time. Quantitative results are presented that compares utilization, performance, and productivity of the approach to what would be achieved by full custom accelerators created through traditional CAD flows using hardware programming models and passing through synthesis

Power Efficient Data-Aware SRAM Cell for SRAM-Based FPGA Architecture

The design of low-power SRAM cell becomes a necessity in today\u27s FPGAs, because SRAM is a critical component in FPGA design and consumes a large fraction of the total power. The present chapter provides an overview of various factors responsible for power consumption in FPGA and discusses the design techniques of low-power SRAM-based FPGA at system level, device level, and architecture levels. Finally, the chapter proposes a data-aware dynamic SRAM cell to control the power consumption in the cell. Stack effect has been adopted in the design to reduce the leakage current. The various peripheral circuits like address decoder circuit, write/read enable circuits, and sense amplifier have been modified to implement a power-efficient SRAM-based FPGA

Operating System Concepts for Reconfigurable Computing: Review and Survey

One of the key future challenges for reconfigurable computing is to enable higher design productivity and a more easy way to use reconfigurable computing systems for users that are unfamiliar with the underlying concepts. One way of doing this is to provide standardization and abstraction, usually supported and enforced by an operating system. This article gives historical review and a summary on ideas and key concepts to include reconfigurable computing aspects in operating systems. The article also presents an overview on published and available operating systems targeting the area of reconfigurable computing. The purpose of this article is to identify and summarize common patterns among those systems that can be seen as de facto standard. Furthermore, open problems, not covered by these already available systems, are identified

FPGA-based architectures for acoustic beamforming with microphone arrays : trends, challenges and research opportunities

Over the past decades, many systems composed of arrays of microphones have been developed to satisfy the quality demanded by acoustic applications. Such microphone arrays are sound acquisition systems composed of multiple microphones used to sample the sound field with spatial diversity. The relatively recent adoption of Field-Programmable Gate Arrays (FPGAs) to manage the audio data samples and to perform the signal processing operations such as filtering or beamforming has lead to customizable architectures able to satisfy the most demanding computational, power or performance acoustic applications. The presented work provides an overview of the current FPGA-based architectures and how FPGAs are exploited for different acoustic applications. Current trends on the use of this technology, pending challenges and open research opportunities on the use of FPGAs for acoustic applications using microphone arrays are presented and discussed

Sequence-To-Sequence Neural Networks Inference on Embedded Processors Using Dynamic Beam Search

Sequence-to-sequence deep neural networks have become the state of the art for a variety of machine learning applications, ranging from neural machine translation (NMT) to speech recognition. Many mobile and Internet of Things (IoT) applications would benefit from the ability of performing sequence-to-sequence inference directly in embedded devices, thereby reducing the amount of raw data transmitted to the cloud, and obtaining benefits in terms of response latency, energy consumption and security. However, due to the high computational complexity of these models, specific optimization techniques are needed to achieve acceptable performance and energy consumption on single-core embedded processors. In this paper, we present a new optimization technique called dynamic beam search, in which the inference complexity is tuned to the difficulty of the processed input sequence at runtime. Results based on measurements on a real embedded device, and on three state-of-the-art deep learning models, show that our method is able to reduce the inference time and energy by up to 25% without loss of accuracy