NTX: A 260 Gflop/sW Streaming Accelerator for Oblivious Floating-Point Algorithms in 22 nm FD-SOI

Fabian Schuiki, Michael Schaffner, L. Benini
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Abstract

In this work we present the first complete design, silicon implementation and measurements in 22 nm FD-SOI of the Network Training Accelerator (NTX) architectural concept [1]. NTX is based on a newly designed partial carrysave "wide-inside" (300 bit) fused multiply-accumulate (FMAC) unit ensuring IEEE 754 compliance and a Root Mean Squared Error 1.7_lower than a conventional 32 bit FPU on long accumulations such as dot products and convolutions.
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NTX:用于22nm FD-SOI的遗忘浮点算法的260 Gflop/sW流加速器
在这项工作中,我们提出了网络训练加速器(NTX)架构概念的第一个完整的设计、硅实现和22纳米FD-SOI的测量[1]。NTX基于新设计的部分载波保存“wide-inside”(300比特)融合乘法累积(FMAC)单元,确保符合IEEE 754标准,并且在长累积(如点积和卷积)上的根均方误差比传统的32位FPU低1.7 _0。
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Multi-carrier Signal Detection using Convolutional Neural Networks An RRAM-based Analog Neuron Design for the Weighted Spiking Neural network NTX: A 260 Gflop/sW Streaming Accelerator for Oblivious Floating-Point Algorithms in 22 nm FD-SOI A Low-Power 20 Gbps Multi-phase MDLL-based Digital CDR with Receiver Equalization Scaling Bit-Flexible Neural Networks
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