Fast and low-power leading-one detectors for energy-efficient logarithmic computing

IF 1.1 4区 计算机科学 Q4 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE
Mohammad Saeed Ansari, Shyama Gandhi, Bruce F. Cockburn, Jie Han
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引用次数: 1

Abstract

The logarithmic number system (LNS) can be used to simplify the computation of arithmetic functions, such as multiplication. This article proposes three leading-one detectors (LODs) to speed up the binary logarithm calculation in the LNS. The first LOD (LOD I) uses a single fixed value to approximate the d least significant bits (LSBs) in the outputs of the LOD. The second design (LOD II) partitions the d LSBs into smaller fields and uses a multiplexer to select the closest approximation to the exact value. These two LODs help with error cancellation as they introduce signed errors for inputs N < 2d. Additionally, a scaling scheme is proposed that scales up the input N < 2d to avoid large approximation errors. Finally, an improved exact LOD (LOD III) is proposed that only passes half of the input N to the LOD; the more significant half is passed if there is at least one ‘1’ in that half; otherwise, the less significant half is passed. Our simulation results show that the 32-bit LOD III can be up to 2.8× more energy-efficient than existing designs in the literature. The Mitchell logarithmic multiplier and a neural network are considered to further illustrate the practicality of the proposed designs.

Abstract Image

用于高能效对数计算的快速低功耗先导检测器
LNS (logarithmic number system)可以简化算术函数(如乘法)的计算。本文提出了三种超前一检测器(lead -one detector, lod)来提高LNS中二进制对数的计算速度。第一个LOD (LOD I)使用一个固定值来近似LOD输出中的d个最低有效位(lbs)。第二种设计(LOD II)将d个llb划分为更小的字段,并使用多路复用器选择最接近确切值的近似值。这两个lod有助于消除错误,因为它们为输入N <引入了有符号错误;2 d。此外,还提出了一种按比例增大输入N <以避免较大的近似误差。最后,提出了一种改进的精确LOD (LOD III),它只将一半的输入N传递给LOD;如果在这一半中至少有一个' 1 ',则通过更重要的一半;否则,通过不太重要的那一半。我们的仿真结果表明,32位LOD III可以比现有文献中的设计节能2.8倍。米切尔对数乘法器和神经网络进一步说明了所提出设计的实用性。
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来源期刊
IET Computers and Digital Techniques
IET Computers and Digital Techniques 工程技术-计算机:理论方法
CiteScore
3.50
自引率
0.00%
发文量
12
审稿时长
>12 weeks
期刊介绍: IET Computers & Digital Techniques publishes technical papers describing recent research and development work in all aspects of digital system-on-chip design and test of electronic and embedded systems, including the development of design automation tools (methodologies, algorithms and architectures). Papers based on the problems associated with the scaling down of CMOS technology are particularly welcome. It is aimed at researchers, engineers and educators in the fields of computer and digital systems design and test. The key subject areas of interest are: Design Methods and Tools: CAD/EDA tools, hardware description languages, high-level and architectural synthesis, hardware/software co-design, platform-based design, 3D stacking and circuit design, system on-chip architectures and IP cores, embedded systems, logic synthesis, low-power design and power optimisation. Simulation, Test and Validation: electrical and timing simulation, simulation based verification, hardware/software co-simulation and validation, mixed-domain technology modelling and simulation, post-silicon validation, power analysis and estimation, interconnect modelling and signal integrity analysis, hardware trust and security, design-for-testability, embedded core testing, system-on-chip testing, on-line testing, automatic test generation and delay testing, low-power testing, reliability, fault modelling and fault tolerance. Processor and System Architectures: many-core systems, general-purpose and application specific processors, computational arithmetic for DSP applications, arithmetic and logic units, cache memories, memory management, co-processors and accelerators, systems and networks on chip, embedded cores, platforms, multiprocessors, distributed systems, communication protocols and low-power issues. Configurable Computing: embedded cores, FPGAs, rapid prototyping, adaptive computing, evolvable and statically and dynamically reconfigurable and reprogrammable systems, reconfigurable hardware. Design for variability, power and aging: design methods for variability, power and aging aware design, memories, FPGAs, IP components, 3D stacking, energy harvesting. Case Studies: emerging applications, applications in industrial designs, and design frameworks.
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