用CORDIC技术实现可动态配置的激活函数，提升AI芯片能效与灵活性。
Retrospective: A CORDIC Based Configurable Activation Function for NN Applications
- 基于移位加法CORDIC设计可重构激活函数核。
- 支持Swish、SoftMax等6种函数，适配多种AI模型。
- 适用于资源受限场景，能效比达98.5%。
此前提出的基于CORDIC的激活函数配置方法，通过功能可重构加速了资源受限系统中ASIC硬件的设计。自提出以来，该神经网络加速新范式在学术界和商业AI处理器中广泛应用。本文回顾该方法的根基，总结近年关键进展，并推出专为现代AI应用需求设计的DA-VINCI AF。新一代动态可配置、精度可调的激活函数核心，利用移位加法CORDIC技术，支持包括Swish、SoftMax、SeLU和GeLU在内的多种激活函数。原有设计已优化用于MAC、Sigmoid和Tanh功能，并集成至ReLU激活函数，形成累加式NEURIC计算单元。这些改进使NEURIC成为面向DNN、RNN/LSTM及Transformer的资源高效向量引擎的核心组件，实现98.5%的高质量结果（QoR）。
原文摘要 · Abstract (English)
A CORDIC-based configuration for the design of Activation Functions (AF) was previously suggested to accelerate ASIC hardware design for resource-constrained systems by providing functional reconfigurability. Since its introduction, this new approach for neural network acceleration has gained widespread popularity, influencing numerous designs for activation functions in both academic and commercial AI processors. In this retrospective analysis, we explore the foundational aspects of this initiative, summarize key developments over recent years, and introduce the DA-VINCI AF tailored for the evolving needs of AI applications. This new generation of dynamically configurable and precision-adjustable activation function cores promise greater adaptability for a range of activation functions in AI workloads, including Swish, SoftMax, SeLU, and GeLU, utilizing the Shift-and-Add CORDIC technique. The previously presented design has been optimized for MAC, Sigmoid, and Tanh functionalities and incorporated into ReLU AFs, culminating in an accumulative NEURIC compute unit. These enhancements position NEURIC as a fundamental component in the resource-efficient vector engine for the realization of AI accelerators that focus on DNNs, RNNs/LSTMs, and Transformers, achieving a quality of results (QoR) of 98.5%.
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