Leading the Blind:Automated Transistor-Level Modeling for FPGA Architects

The design and development of innovative FPGA architectures hinge on the flexibility of its toolchain. Retargetable toolchains, like the Verilog-to-Routing (VTR) flow, have been developed to enable the testing of new FPGAs by mapping circuits onto easily-described and possibly theoretical architectures. However, in reality, the difficulty extends beyond having CAD tools that support the architectural changes: it is equally important for FPGA architects to be able to produce reliable delay and area models for these tools. In addition to having acute architectural intuitions, designing and optimizing the circuit at the transistor-level requires architects to have, as well, a particular set of electrical engineering skills and expertise. The process is also painstaking and time-consuming, rendering the comparison of a variety of architectures or the exploration of a wide design space quite complicated and even impossible in practice. In this work, we present a novel approach to model the delay and area of FPGA architectures with various structures and characteristics, quickly and with acceptable accuracy. Abstracting from the user the transistor-level design and optimization that normally accompany the model- ing process, this approach, called FPRESSO, can be used by any architect without prerequisites. We take inspiration from the way a standard-cell flow performs large-scale transistor-size optimization and apply the same concepts to FPGAs, only at a coarser granularity. Skilled designers prepare for FPRESSO a set of locally optimized libraries of basic parameterizable components with a variety of drive strengths. Then, inexperienced users specify arbitrary FPGA architectures as interconnects of these basic components. The architecture is globally optimized, within minutes, through a standard logic synthesis tool, by choosing the most fitting version of each cell and adding buffers wherever appropriate. The resulting delay and area characteristics are automatically returned, in a format suitable for the VTR flow. A correct modeling of any architecture requires not only an optimization of the logic components, but also a proper modeling of the wires connecting these components. This does not only include measuring the length of the wires to determine their respective resistance and capacitance, but also, minimizing their length to reduce the wireload effect on the overall performance. To that end, FPRESSO features an automatic and generic wire modeling approach based on a simulated annealing floorplanning algorithm, to estimate the wires between the different components of the FPGA architecture. To evaluate the results of FPRESSO and confirm the validity of its modeled architectures, we use it to explore a wide range of FPGA architectures. First, we repeat a known study that helped set the standards on the optimal Look-Up-Table (LUT) and cluster size for conventional FPGAs. We show, by comparing with the results of the study, that modeling in FPRESSO preserves the very same trends and conclusions, with significantly less effort. We then extend the search space to cover fracturable LUTs and sparse crossbars, and show how FPRESSO makes the exploration of a huge search space not only possible but easy, efficient, and affordable, for any class of VTR users

Analyzing the divide between FPGA academic and commercial results

The pinnacle of success for academic work is often achieved by having impact on commercial products. In order to have a successful transfer bridge, academic evaluation flows need to provide representative results of similar quality to commercial flows. A majority of publications in FPGA research use the same set of known academic CAD tools and benchmarks to evaluate new architecture and tool ideas. However, it is not clear whether the claims in academic publications based on these tools and benchmarks translate to real benefits in commercial products. In this work we compare the latest Xilinx commercial tools and products with these well-known academic tools to identify the gap in the major figures of merit. Our results show that there is a significant 2.2X gap in speed-performance for similar process technology. We have also identified the area-efficiency and runtime divide between commercial and academic tools to be 5% and 2.2X, respectively. We show that it is possible to improve portions of the academic flow such as ABC logic optimization to match the quality of commercial tools at the expense of additional runtime. Our results also show that depth reduction, which is often used as the main figure of merit for logic optimization papers does not translate to post-routing timing improvements. We finally discuss the differences between academic and commercial benchmark designs. We explain the main differences and trends that may influence the topic choice and conclusions of academic research. This work emphasizes how difficult it is to identify the relevant FPGA academic work that can provide meaningful benefits for commercial products

Designing energy-efficient computing systems using equalization and machine learning

As technology scaling slows down in the nanometer CMOS regime and mobile computing becomes more ubiquitous, designing energy-efficient hardware for mobile systems is becoming increasingly critical and challenging. Although various approaches like near-threshold computing (NTC), aggressive voltage scaling with shadow latches, etc. have been proposed to get the most out of limited battery life, there is still no “silver bullet” to increasing power-performance demands of the mobile systems. Moreover, given that a mobile system could operate in a variety of environmental conditions, like different temperatures, have varying performance requirements, etc., there is a growing need for designing tunable/reconfigurable systems in order to achieve energy-efficient operation. In this work we propose to address the energy- efficiency problem of mobile systems using two different approaches: circuit tunability and distributed adaptive algorithms. Inspired by the communication systems, we developed feedback equalization based digital logic that changes the threshold of its gates based on the input pattern. We showed that feedback equalization in static complementary CMOS logic enabled up to 20% reduction in energy dissipation while maintaining the performance metrics. We also achieved 30% reduction in energy dissipation for pass-transistor digital logic (PTL) with equalization while maintaining performance. In addition, we proposed a mechanism that leverages feedback equalization techniques to achieve near optimal operation of static complementary CMOS logic blocks over the entire voltage range from near threshold supply voltage to nominal supply voltage. Using energy-delay product (EDP) as a metric we analyzed the use of the feedback equalizer as part of various sequential computational blocks. Our analysis shows that for near-threshold voltage operation, when equalization was used, we can improve the operating frequency by up to 30%, while the energy increase was less than 15%, with an overall EDP reduction of ≈10%. We also observe an EDP reduction of close to 5% across entire above-threshold voltage range. On the distributed adaptive algorithm front, we explored energy-efficient hardware implementation of machine learning algorithms. We proposed an adaptive classifier that leverages the wide variability in data complexity to enable energy-efficient data classification operations for mobile systems. Our approach takes advantage of varying classification hardness across data to dynamically allocate resources and improve energy efficiency. On average, our adaptive classifier is ≈100× more energy efficient but has ≈1% higher error rate than a complex radial basis function classifier and is ≈10× less energy efficient but has ≈40% lower error rate than a simple linear classifier across a wide range of classification data sets. We also developed a field of groves (FoG) implementation of random forests (RF) that achieves an accuracy comparable to Convolutional Neural Networks (CNN) and Support Vector Machines (SVM) under tight energy budgets. The FoG architecture takes advantage of the fact that in random forests a small portion of the weak classifiers (decision trees) might be sufficient to achieve high statistical performance. By dividing the random forest into smaller forests (Groves), and conditionally executing the rest of the forest, FoG is able to achieve much higher energy efficiency levels for comparable error rates. We also take advantage of the distributed nature of the FoG to achieve high level of parallelism. Our evaluation shows that at maximum achievable accuracies FoG consumes ≈1.48×, ≈24×, ≈2.5×, and ≈34.7× lower energy per classification compared to conventional RF, SVM-RBF , Multi-Layer Perceptron Network (MLP), and CNN, respectively. FoG is 6.5× less energy efficient than SVM-LR, but achieves 18% higher accuracy on average across all considered datasets

Design and application of reconfigurable circuits and systems

Open Acces

Energy-Efficient Digital Circuit Design using Threshold Logic Gates

abstract: Improving energy efficiency has always been the prime objective of the custom and automated digital circuit design techniques. As a result, a multitude of methods to reduce power without sacrificing performance have been proposed. However, as the field of design automation has matured over the last few decades, there have been no new automated design techniques, that can provide considerable improvements in circuit power, leakage and area. Although emerging nano-devices are expected to replace the existing MOSFET devices, they are far from being as mature as semiconductor devices and their full potential and promises are many years away from being practical. The research described in this dissertation consists of four main parts. First is a new circuit architecture of a differential threshold logic flipflop called PNAND. The PNAND gate is an edge-triggered multi-input sequential cell whose next state function is a threshold function of its inputs. Second a new approach, called hybridization, that replaces flipflops and parts of their logic cones with PNAND cells is described. The resulting \hybrid circuit, which consists of conventional logic cells and PNANDs, is shown to have significantly less power consumption, smaller area, less standby power and less power variation. Third, a new architecture of a field programmable array, called field programmable threshold logic array (FPTLA), in which the standard lookup table (LUT) is replaced by a PNAND is described. The FPTLA is shown to have as much as 50% lower energy-delay product compared to conventional FPGA using well known FPGA modeling tool called VPR. Fourth, a novel clock skewing technique that makes use of the completion detection feature of the differential mode flipflops is described. This clock skewing method improves the area and power of the ASIC circuits by increasing slack on timing paths. An additional advantage of this method is the elimination of hold time violation on given short paths. Several circuit design methodologies such as retiming and asynchronous circuit design can use the proposed threshold logic gate effectively. Therefore, the use of threshold logic flipflops in conventional design methodologies opens new avenues of research towards more energy-efficient circuits.Dissertation/ThesisDoctoral Dissertation Computer Science 201

A Micro Power Hardware Fabric for Embedded Computing

Field Programmable Gate Arrays (FPGAs) mitigate many of the problemsencountered with the development of ASICs by offering flexibility, faster time-to-market, and amortized NRE costs, among other benefits. While FPGAs are increasingly being used for complex computational applications such as signal and image processing, networking, and cryptology, they are far from ideal for these tasks due to relatively high power consumption and silicon usage overheads compared to direct ASIC implementation. A reconfigurable device that exhibits ASIC-like power characteristics and FPGA-like costs and tool support is desirable to fill this void. In this research, a parameterized, reconfigurable fabric model named as domain specific fabric (DSF) is developed that exhibits ASIC-like power characteristics for Digital Signal Processing (DSP) style applications. Using this model, the impact of varying different design parameters on power and performance has been studied. Different optimization techniques like local search and simulated annealing are used to determine the appropriate interconnect for a specific set of applications. A design space exploration tool has been developed to automate and generate a tailored architectural instance of the fabric.The fabric has been synthesized on 160 nm cell-based ASIC fabrication process from OKI and 130 nm from IBM. A detailed power-performance analysis has been completed using signal and image processing benchmarks from the MediaBench benchmark suite and elsewhere with comparisons to other hardware and software implementations. The optimized fabric implemented using the 130 nm process yields energy within 3X of a direct ASIC implementation, 330X better than a Virtex-II Pro FPGA and 2016X better than an Intel XScale processor

A Comprehensive Workflow for General-Purpose Neural Modeling with Highly Configurable Neuromorphic Hardware Systems

In this paper we present a methodological framework that meets novel requirements emerging from upcoming types of accelerated and highly configurable neuromorphic hardware systems. We describe in detail a device with 45 million programmable and dynamic synapses that is currently under development, and we sketch the conceptual challenges that arise from taking this platform into operation. More specifically, we aim at the establishment of this neuromorphic system as a flexible and neuroscientifically valuable modeling tool that can be used by non-hardware-experts. We consider various functional aspects to be crucial for this purpose, and we introduce a consistent workflow with detailed descriptions of all involved modules that implement the suggested steps: The integration of the hardware interface into the simulator-independent model description language PyNN; a fully automated translation between the PyNN domain and appropriate hardware configurations; an executable specification of the future neuromorphic system that can be seamlessly integrated into this biology-to-hardware mapping process as a test bench for all software layers and possible hardware design modifications; an evaluation scheme that deploys models from a dedicated benchmark library, compares the results generated by virtual or prototype hardware devices with reference software simulations and analyzes the differences. The integration of these components into one hardware-software workflow provides an ecosystem for ongoing preparative studies that support the hardware design process and represents the basis for the maturity of the model-to-hardware mapping software. The functionality and flexibility of the latter is proven with a variety of experimental results

The IPS fidelity scale as a guideline to implement Supported Employment

info:eu-repo/semantics/publishe