IP Core for Efficient Zero-Run Length Compression of CNN Feature Maps

Convolutional Neural Networks (CNNs) are becoming a fundamental tool for machine learning. High performance and energy efficiency are of great importance for deployments of CNNs in many embedded applications. Energy consumption during CNN processing is dominated by memory access and since large networks do not fit in on-chip storage, they require expensive DRAM access. This paper introduces an universal Output Stream Manager (OSM) which can be used to compress and format data coming from a CNN accelerator and reduce external memory access. The OSM exploits the sparsity of data and implements two Zero-Run Length encoding algorithms and can be easily reconfigured to optimize usage for different CNN layers

Load Balancing of Large Distribution Network Model Calculations

Performance measurement and evaluation study of calculations based on load flow analysis in power distribution network is presented. The focus is on the choice of load index as it is the basic input for efficient dynamic load balancing. The basic description of problem along with the proposed architecture is given. Different server resources are inspected and analyzed while running calculations, and based on this investigation, recommendations regarding the choice of load index are made. Short description of used static and dynamic load balancing algorithms is given and the proposition of load index choice is supported by tests run on large real-world power distribution network models

An Optimal Initial Partitioning of Large Data Model in Utility Management Systems

Modern Utility Management Systems which utilize multiprocessor systems for efficient processing of large data model are considered in this paper. The necessary preconditions for the efficient calculation are optimal load balancing of processors and data model partitioning among processors. The novel multilevel Super-Roots (SR) algorithm was developed to improve existing algorithms (e. i. METIS) for initial partitioning of data model. The proposed algorithms are applied on data model describing large electricity power distribution network. Experiments show that SR algorithm achieves better results than METIS multilevel algorithm in many cases