SECO: A Scalable Accuracy Approximate Exponential Function Via Cross-Layer Optimization
SECO: A Scalable Accuracy Approximate Exponential Function Via Cross-Layer Optimization
复制标题
DOI：
10.1109/islped.2019.8824959
复制
发表时间：
2019-07
期刊：
影响因子：
--
通讯作者：
Di Wu;Tianen Chen;Chienfu Chen;Oghenefego Ahia;Joshua San Miguel;Mikko H. Lipasti;Younghyun Kim
中科院分区：
文献类型：
--
作者：
Di Wu;Tianen Chen;Chienfu Chen;Oghenefego Ahia;Joshua San Miguel;Mikko H. Lipasti;Younghyun Kim
从信号处理到新兴的深度神经网络，一系列应用都表现出固有的错误弹性。对于这样的应用程序，近似计算通过使用大大简化的硬件产生稍微不准确的结果，为节能计算开辟了新的可能性。采用这种方法，各种基本算术单元（如加法器和乘法器）已被有效地重新设计，以便为许多容错应用程序生成近似结果。在这项工作中，我们提出SECO，一个近似指数函数单元（EFU）。幂运算是许多信号处理应用的关键运算，在峰值神经元模型中更为重要，但其节能实现尚未得到充分探索。我们还介绍了SECO的跨层设计方法，以优化能量-精度权衡。在算法层面，SECO提供了基于近似泰勒展开的能源效率和精度之间的运行时缩放，其中通过在设计时使用离散梯度下降优化参数，将误差最小化。在电路层面，我们的误差分析方法有效地探索了设计空间，在设计时选择能量精度最优的近似乘法器。同时，跨层设计和运行时优化方法能够产生节能和精确的近似EFU设计，在每指数运算3.73 pJ的功耗下，准确率高达99.7%。在自适应指数积分-火神经元模型上对SECO进行了评估，与精确神经元模型相比，其时序误差仅为0.002%，值误差为0.067%。
From signal processing to emerging deep neural networks, a range of applications exhibit intrinsic error resilience. For such applications, approximate computing opens up new possibilities for energy-efficient computing by producing slightly inaccurate results using greatly simplified hardware. Adopting this approach, a variety of basic arithmetic units, such as adders and multipliers, have been effectively redesigned to generate approximate results for many error-resilient applications.In this work, we propose SECO, an approximate exponential function unit (EFU). Exponentiation is a key operation in many signal processing applications and more importantly in spiking neuron models, but its energy-efficient implementation has been inadequately explored. We also introduce a cross-layer design method for SECO to optimize the energy-accuracy trade-off. At the algorithm level, SECO offers runtime scaling between energy efficiency and accuracy based on approximate Taylor expansion, where the error is minimized by optimizing parameters using discrete gradient descent at design time. At the circuit level, our error analysis method efficiently explores the design space to select the energy-accuracy-optimal approximate multiplier at design time. In tandem, the cross-layer design and runtime optimization method are able to generate energy-efficient and accurate approximate EFU designs that are up to 99.7% accurate at a power consumption of 3.73 pJ per exponential operation. SECO is also evaluated on the adaptive exponential integrate-and-fire neuron model, yielding only 0.002% timing error and 0.067% value error compared to the precise neuron model.