This article presents OPTIMO, a 65-nm, 16-b, fully programmable, spatial-array processor with 49 cores and a hierarchical multi-cast network for solving distributed optimizations via the alternating direction method of multipliers (ADMM). ADMM is a projection-based method for solving generic-constrained optimizations’ problems. In essence, it relies upon decomposing the decision vector into subvectors, updating sequentially by minimizing an augmented Lagrangian function, and eventually updating the Lagrange multiplier. The ADMM algorithm has typically been used for solving problems in which the decision variable is decomposed into two or multiple subvectors. We demonstrate six template algorithms and their applications and measure a peak energy efficiency of 279 GOPS/W.

中文翻译：

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OPTIMO：具有片上网络的 65-nm 279-GOPS/W 16-b 可编程空间阵列处理器，用于通过乘法器的交替方向方法解决分布式优化问题

本文介绍了 OPTIMO，这是一款 65 纳米、16 位、完全可编程的空间阵列处理器，具有 49 个内核和分层多播网络，用于通过乘法器的交替方向法 (ADMM) 解决分布式优化问题。ADMM 是一种基于投影的方法，用于解决通用约束优化问题。本质上，它依赖于将决策向量分解为子向量，通过最小化增广拉格朗日函数依次更新，并最终更新拉格朗日乘数。ADMM 算法通常用于解决决策变量分解为两个或多个子向量的问题。我们展示了六种模板算法及其应用，并测量了 279 GOPS/W 的峰值能效。