A geometric program (GP) is a type of mathematical optimization problem characterized by objective and constraint functions that have a special form. Recently developed solution methods can solve even large-scale GPs extremely efficiently and reliably; at the same time a number of practical problems, particularly in circuit design, have been found to be equivalent to (or well approximated by) GPs. Putting these two together, we get effective solutions for the practical problems. The basic approach in GP modeling is to attempt to express a practical problem, such as an engineering analysis or design problem, in GP format. In the best case, this formulation is exact; when this is not possible, we settle for an approximate formulation. This tutorial paper collects together in one place the basic background material needed to do GP modeling. We start with the basic definitions and facts, and some methods used to transform problems into GP format. We show how to recognize functions and problems compatible with GP, and how to approximate functions or data in a form compatible with GP (when this is possible). We give some simple and representative examples, and also describe some common extensions of GP, along with methods for solving (or approximately solving) them.

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A tutorial on geometric programming

IEEE transactions on computer-aided design of integrated circuits and systems : a publication of the IEEE Circuits and Systems Society

The main characteristic of Reconfigurable Computing is the presence of hardware that can be reconfigured to implement specific functionality more suitable for specially tailored hardware than on a simple uniprocessor. Reconfigurable computing systems join microprocessors and programmable hardware in order to take advantage of the combined strengths of hardware and software and have been used in applications ranging from embedded systems to high performance computing. Many of the fundamental theories have been identified and used by the Hardware/Software Co-Design research field. Although the same background ideas are shared in both areas, they have different goals and use different approaches.This book is intended as an introduction to the entire range of issues important to reconfigurable computing, using FPGAs as the context, or "computing vehicles" to implement this powerful technology. It will take a reader with a background in the basics of digital design and software programming and provide them with the knowledge needed to be an effective designer or researcher in this rapidly evolving field.

· Treatment of FPGAs as computing vehicles rather than glue-logic or ASIC substitutes
· Views of FPGA programming beyond Verilog/VHDL
· Broad set of case studies demonstrating how to use FPGAs in novel and efficient ways

Reconfigurable Computing: The Theory and Practice of FPGA-Based Computation

In this paper, we presen t the design and use of a comprehensiv e framework, SimplePower, for ev aluating the effect of high-level algorithmic, architectural, and compilation trade-offs on energy. An execution-driven, cycle-accurate RT lev el energy estimation tool that uses transition sensitive energy models forms the cornerstone of this framework. SimplePower also pro vides the energy consumed in the memory system and on-chip buses using analytical energy models.We presen t the use of SimplePower to evaluate the impact of a new selective gated pipeline register optimization, a high-level data transformation and a pow er-conscious post compilation optimization (register relabeling) on the datapath, memory and on-chip bus energy, respectively. We find that these three optimizations reduce the energy by 18-36% in the datapath, 62% in the memory system and 12% in the instruction cache data bus, respectively.

The design and use of simplepower: a cycle-accurate energy estimation tool

High-performance parallel computer architecture and systems have been improved at a phenomenal rate. In the meantime, VLSI computer-aided design (CAD) software for multibillion-transistor IC design has become increasingly complex and requires prohibitively high computational resources. Recent studies have shown that, numerous CAD problems, with their high computational complexity, can greatly benefit from the fast-increasing parallel computation capabilities. However, parallel programming imposes big challenges for CAD applications. Fully exploiting the computational power of emerging general-purpose and domain-specific multicore/many-core processor systems, calls for fundamental research and engineering practice across every stage of parallel CAD design, from algorithm exploration, programming models, design-time and run-time environment, to CAD applications, such as verification, optimization, and simulation. This journal special section will cover recent progress on parallel CAD research, including algorithm foundations, programming models, parallel architectural-specific optimization, and verification. More specifically, papers with in-depth and extensive coverage of the following topics will be considered, as well as other topics relevant to the design of parallel CAD algorithms and software tools. 1. Parallel algorithm design and specification for CAD applications 2. Parallel programming models and languages of particular use in CAD 3. Runtime support and performance optimization for CAD applications 4. Parallel architecture-specific design and optimization for CAD applications 5. Parallel program debugging and verification techniques particularly relevant for CAD The papers should be submitted via the Manuscript Central website and should adhere to standard ACM TODAES formatting requirements (http://todaes.acm.org/). The page count limit is 25.

Parallel CAD: Algorithm Design and Programming Special Section Call for Papers TODAES: ACM Transactions on Design Automation of Electronic Systems

Noise, as well as area, delay, and power, is one of the most important concerns in the design of deep submicrometer integrated circuits. Currently existing algorithms do not handle simultaneous switching conditions of signals for noise minimization. In this paper, we model not only physical coupling capacitance, but also simultaneous switching behavior for noise optimization. Based on Lagrangian relaxation, we present an algorithm which can optimally solve the simultaneous noise, area, delay, and power optimization problem by sizing circuit components. Our algorithm, with linear memory requirement and linear runtime, is very effective and efficient. For example, for a circuit of 6144 wires and 3512 gates, our algorithm solves the simultaneous optimization problem using only 2.1-MB memory and 19.4-min runtime to achieve the precision of within 1% error on a SUN Spare Ultra-I workstation.

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Crosstalk-driven interconnect optimization by simultaneous gate and wire sizing

This paper shows that the worst case switching pattern that incurs the longest bus delay while considering the RLC effect is quite different from that while considering the RC effect alone. It implies that the existing encoding schemes based on the RC model may not improve or possibly worsen the delay when the inductance effects become dominant. A bus-invert method is also proposed to reduce the on-chip bus delay based on the RLC model. Simulation results show that the proposed encoding scheme significantly reduces the worst case coupling delay of the inductance-dominated buses

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RLC Coupling-Aware Simulation and On-Chip Bus Encoding for Delay Reduction

A partial scan approach that aims to reduce both area overhead and performance degradation caused by test logic is presented. Given a target speed and an initial design that meets the target, the algorithm selects a minimum set of scan flip-flops, if they exist, that (1) will break all sequential cycles and (2) will not violate the performance requirement after the scan logic is added. If such a set does not exist, the algorithm will find a set of scan flip-flops in which (1) all sequential cycles are broken and (2) the total area increase caused by the scan logic and the subsequent performance optimization is minimized, For circuits synthesized by automatic synthesis tools, the authors suggest a novel design flow, which selects/inserts the partial scan logic after area optimization, but before performance optimization. For meeting both performance and testability requirements, this design flow produces designs with less area increase than the traditional design flow, which considers testability and adds test logic after performance optimization. Experimental results on the ISCA'89 sequential circuits are presented as well as comparisons between the proposed method and previous methods. >

Timing-driven partial scan

This paper presents a formal method for equivalence checking between the descriptions before and after scheduling in high-level synthesis (HLS). Both descriptions are represented by finite state machine with datapaths (FSMDs) and are then characterized through finite sets of paths. The main target of our proposed method is to verify scheduling employing code transformations -- such as speculation and common subexpression extraction (CSE), across basic block (BB) boundaries - which have not been properly addressed in the past. Nevertheless, our method can verify typical BB-based and path-based scheduling as well. The experimental results demonstrate that the proposed method can indeed outperform an existing state-of-the-art equivalence checking algorithm.

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Equivalence checking of scheduling with speculative code transformations in high-level synthesis

Network-on-chip is a new design paradigm for designing core based system-on-chip. It features high degree of reusability and scalability. In this paper, we propose a switch which employs the latency insensitive concepts and applies the round-robin scheduling techniques to achieve high communication resource utilization. Based on the assumptions of the 2D-mesh network topology constructed by the switch, this work not only models the communication and the contention effect of the network, but develops a communication-driven task binding algorithm that employs the divide and conquer strategy to map applications onto the multiprocessor system-on-chip. The algorithm attempts to derive a binding of tasks such that the overall system throughput is maximized. To compare with the task binding without consideration of communication and contention effect, the experimental results demonstrate that the overall improvement of the system throughput is 20% for 844 test cases.

Jing-Yang Jou

Papers

Crosstalk-driven interconnect optimization by simultaneous gate and wire sizing

RLC Coupling-Aware Simulation and On-Chip Bus Encoding for Delay Reduction

Timing-driven partial scan

Equivalence checking of scheduling with speculative code transformations in high-level synthesis

Communication-driven task binding for multiprocessor with latency insensitive network-on-chip