DDPMnet:基于全数字脉冲密度的DNN架构,228栅极当量/MAC单元,28-TOPS/W和1.5-TOPS/mm2在40nm

Animesh Gupta, V. Rajanna, Thoithoi Salam, Saurabh Jain, O. Aiello, P. Crovetti, M. Alioto
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引用次数: 2

摘要

低成本边缘器件[1]-[8]要求DNN加速器的能量和面积效率不断提高。两者都直接受益于MAC单元(神经元)复杂性的降低,这要归功于计算面积和能量的减少以及互连结构。不幸的是,在需要灵活性(例如精确缩放)的实际情况下,每个神经元的面积和能量成本会进一步增加,最终限制了成本和功耗的降低。在这项工作中,引入了基于脉冲密度数据表示的DNN加速全数字DDPMnet架构,以将门数/MAC单元从数千个范围减少到数百个(图1)。所提出的架构删除了MAC单元(例如乘法器)中的任何算术块,同时保留了基于标准单元设计的优点。
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DDPMnet: All-Digital Pulse Density-Based DNN Architecture with 228 Gate Equivalents/MAC Unit, 28-TOPS/W and 1.5-TOPS/mm2 in 40nm
Relentless advances in DNN accelerator energy and area efficiency are demanded in low-cost edge devices [1]–[8]. Both directly benefit from the reduction in the complexity of MAC units (neurons), thanks to the reduction in area and energy of computations and the interconnect fabric. Unfortunately, such area and energy cost per neuron further increases in practical cases where flexibility is needed (e.g., precision scaling), ultimately limiting cost and power reductions. In this work, the all-digital DDPMnet architecture for DNN acceleration based on a pulse density data representation is introduced to reduce the gate count/MAC unit from the thousand range to few hundreds (Fig. 1). The proposed architecture removes any arithmetic block from MAC units (e.g., multipliers), while retaining the advantages of standard cell based design.
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