FUSE: Front-End User Framework for O/S Abstraction of Hardware Accelerators

Abstract—SoCs can be implemented on a single FPGA, offering designers a unique opportunity for Embedded Sys-tems. Instead of defining a fixed architecture early in the design process, the reconfigurable platform allows architec-tural redesign to meet the system’s specific needs. However, the ability to instantiate new modules in the reconfigurable hardware provides a unique set of challenges for integration, particularly to the software (SW) designer. Specifically, the Operating System (OS) cannot automatically abstract these platform changes without redesign. In this paper, we present FUSE, a framework for HW accelerator abstraction that provides: 1) transparency to the SW designer at the application level; and 2) OS support for easy HW accelerator integration. We illustrate FUSE as an API for an embedded Linux OS with POSIX threads on Xilinx’s MicroBlaze on a Virtex5. For three different applications and HW accelerators, we achieve performance speedups ranging from 6.4-37x. I

Operating system abstractions of hardware accelerators on field-programmable gate arrays

Traditionally, one of the main functions of the Operating System (OS) is to abstract the programming model from the low level details of the specic HW platform resources. However, in an FPGA-based SoC with HW accelerators, even with an OS layer, there is no unied HW/SW framework that provides: 1) transparency to the SW designer at the application level; and 2) an interface and OS support for easy HW accelerator integration by the HW designer at the platform level. This thesis presents a Front-end USEr framework, called FUSE, that introduces a set of policies and mechanisms for HW accelerator abstraction. We illustrate FUSE as an API for an embedded Linux OS with POSIX threads on Xilinx\u27s MicroBlaze on a Virtex5 FPGA. For three dierent applications and HW accelerators, we achieve performance speedups ranging from 5.8-9.0x

Missing-values imputation algorithms for microarray gene expression data

In gene expression studies, missing values are a common problem with important consequences for the interpretation of the final data (Satija et al., Nat Biotechnol 33(5):495, 2015). Numerous bioinformatics examination tools are used for cancer prediction, including the data set matrix (Bailey et al., Cell 173(2):371–385, 2018); thus, it is necessary to resolve the problem of missing-values imputation. This chapter presents a review of the research on missing-values imputation approaches for gene expression data. By using local and global correlation of the data, we were able to focus mostly on the differences between the algorithms. We classified the algorithms as global, hybrid, local, or knowledge-based techniques. Additionally, this chapter presents suitable assessments of the different approaches. The purpose of this review is to focus on developments in the current techniques for scientists rather than applying different or newly developed algorithms with identical functional goals. The aim was to adapt the algorithms to the characteristics of the data