In edge computing, suppressing data size is a challenge for machine learning models that perform complex tasks such as autonomous driving, in which computational resources (speed, memory size and power) are limited. Efficient lossy compression of matrix data has been introduced by decomposing it into the product of an integer and real matrices. However, its optimisation is difficult as it requires simultaneous optimisation of an integer and real variables. In this paper, we improve this optimisation by utilising recently developed black-box optimisation (BBO) algorithms with an Ising solver for integer variables. In addition, the algorithm can be used to solve mixed-integer programming problems that are linear and non-linear in terms of real and integer variables, respectively. The differences between the choice of Ising solvers (simulated annealing, quantum annealing and simulated quenching) and the strategies of the BBO algorithms (BOCS, FMQA and their variations) are discussed for further development of the BBO techniques.
翻译:在边缘计算中,抑制数据大小对于执行诸如自动驱动等复杂任务的机器学习模型来说是一项挑战,因为在这种模型中,计算资源(速度、内存大小和功率)有限。通过将矩阵数据分解成一个整数和真实矩阵的产物,对矩阵数据进行了有效的损耗压缩。然而,它的优化是困难的,因为它需要同时优化一个整数和真实变量。在本文中,我们通过利用最近开发的黑箱优化算法和一个Ising求解器对整数变量进行优化。此外,该算法可以用来解决在实际变量和整数变量方面线性和非线性混合整数的编程问题。为了进一步开发BBO技术,将讨论Ising解决器的选择(模拟肛门、量analing和模拟排泄法)与BBO算法的战略(BOCS、FQA及其变式)之间的差异。