An efficient multiple precision floating-point Multiply-Add Fused unit

Multiply-Add Fused (MAF) units play a key role in the processor's performance for a variety of applications. The objective of this paper is to present a multi-functional, multiple precision floating-point Multiply-Add Fused (MAF) unit. The proposed MAF is reconfigurable and able to execute a quadruple precision MAF instruction, or two double precision instructions, or four single precision instructions in parallel. The MAF architecture features a dual-path organization reducing the latency of the floating-point add (FADD) instruction and utilizes the minimum number of operating components to keep the area low. The proposed MAF design was implemented on a 65 nm silicon process achieving a maximum operating frequency of 293.5 MHz at 381 mW power

Multi-functional floating-point MAF designs with dot product support

This paper presents multi-functional double-precision and quadruple-precision floating-point multiply-add fused (FPMAF) designs. The double-precision FPMAF design can execute adouble-precision floating-point multiply-add, or two single-precision floating-point multiplications, or a single-precision floating-point dot product. The quadruple-precision FPMAF can perform similar operations with quadruple, double and single precision operands. These architectures can perform a dot-product operation two times or more faster than a basic FPMAF design. The presented multi-functional designs are compared with basic double-precision and quadruple-precision FPMAF designs by ASIC syntheses. The syntheses results show that the proposed double-precision implementation has 8%more area than a standard double-precision FPMAF implementation, and the proposed quadruple-precision design has 12.5% more area than a standard quadruple-precision FPMAF. Both of the proposed designs have one more pipeline stage compared to the basic designs. © 2007 Elsevier Ltd. All rights reserved

Flexible Multiple-Precision Fused Arithmetic Units for Efficient Deep Learning Computation

Deep Learning has achieved great success in recent years. In many fields of applications, such as computer vision, biomedical analysis, and natural language processing, deep learning can achieve a performance that is even better than human-level. However, behind this superior performance is the expensive hardware cost required to implement deep learning operations. Deep learning operations are both computation intensive and memory intensive. Many research works in the literature focused on improving the efficiency of deep learning operations. In this thesis, special focus is put on improving deep learning computation and several efficient arithmetic unit architectures are proposed and optimized for deep learning computation. The contents of this thesis can be divided into three parts: (1) the optimization of general-purpose arithmetic units for deep learning computation; (2) the design of deep learning specific arithmetic units; (3) the optimization of deep learning computation using 3D memory architecture. Deep learning models are usually trained on graphics processing unit (GPU) and the computations are done with single-precision floating-point numbers. However, recent works proved that deep learning computation can be accomplished with low precision numbers. The half-precision numbers are becoming more and more popular in deep learning computation due to their lower hardware cost compared to the single-precision numbers. In conventional floating-point arithmetic units, single-precision and beyond are well supported to achieve a better precision. However, for deep learning computation, since the computations are intensive, low precision computation is desired to achieve better throughput. As the popularity of half-precision raises, half-precision operations are also need to be supported. Moreover, the deep learning computation contains many dot-product operations and therefore, the support of mixed-precision dot-product operations can be explored in a multiple-precision architecture. In this thesis, a multiple-precision fused multiply-add (FMA) architecture is proposed. It supports half/single/double/quadruple-precision FMA operations. In addition, it also supports 2-term mixed-precision dot-product operations. Compared to the conventional multiple-precision FMA architecture, the newly added half-precision support and mixed-precision dot-product only bring minor resource overhead. The proposed FMA can be used as general-purpose arithmetic unit. Due to the support of parallel half-precision computations and mixed-precision dot-product computations, it is especially suitable for deep learning computation. For the design of deep learning specific computation unit, more optimizations can be performed. First, a fixed-point and floating-point merged multiply-accumulate (MAC) unit is proposed. As deep learning computation can be accomplished with low precision number formats, the support of high precision floating-point operations can be eliminated. In this design, the half-precision floating-point format is supported to provide a large dynamic range to handle small gradients for deep learning training. For deep learning inference, 8-bit fixed-point 2-term dot-product computation is supported. Second, a flexible multiple-precision MAC unit architecture is proposed. The proposed MAC unit supports both fixed-point operations and floating-point operations. For floating-point format, the proposed unit supports one 16-bit MAC operation or sum of two 8-bit multiplications plus a 16-bit addend. To make the proposed MAC unit more versatile, the bit-width of exponent and mantissa can be flexibly exchanged. By setting the bit-width of exponent to zero, the proposed MAC unit also supports fixed-point operations. For fixed-point format, the proposed unit supports one 16-bit MAC or sum of two 8-bit multiplications plus a 16-bit addend. Moreover, the proposed unit can be further divided to support sum of four 4-bit multiplications plus a 16-bit addend. At the lowest precision, the proposed MAC unit supports accumulating of eight 1-bit logic AND operations to enable the support of binary neural networks. Finally, a MAC architecture based on the posit format, a promising numerical format in deep learning computation, is proposed to facilitate the use of posit format in deep learning computation. In addition to the above mention arithmetic units, an improved hybrid memory cube (HMC) architecture is proposed for weight-sharing deep neural network processing. By modifying the HMC instruction set and HMC logic layer, the major part of the deep learning computation can be accomplished inside memory. The proposed design reduces the memory bandwidth requirements and thus reduces the energy consumed by memory data transfer