Model-Based Hardware-Software Codesign of ECT Digital Processing Unit

Abstract

Image reconstruction algorithm and its controller constitute the main modules of the electrical capacitance tomography (ECT) system; in order to achieve the trade-off between the attainable performance and the flexibility of the image reconstruction and control design of the ECT system, hardware-software codesign of a digital processing unit (DPU) targeting FPGA system-on-chip (SoC) is presented. Design and implementation of software and hardware components of the ECT-DPU and their integration and verification based on the model-based design (MBD) paradigm are proposed. The inner-product of large vectors constitutes the core of the majority of these ECT image reconstruction algorithms. Full parallel implementation of large vector multiplication on FPGA consumes a huge number of resources and incurs long combinational path delay. The proposed MBD of the ECT-DPU tackles this problem by crafting a parametric segmented parallel inner-product architecture so as to work as the shared hardware core unit for the parallel matrix multiplication in the image reconstruction and control of the ECT system. This allowed the parameterized core unit to be configured at system-level to tackle large matrices with the segment length working as a design degree of freedom. It allows the trade-off between performance and resource usage and determines the level of computation parallelism. Using MBD with the proposed segmented architecture, the system design can be flexibly tailored to the designer specifications to fulfill the required performance while meeting the resources constraint. In the linear-back projection image reconstruction algorithm, the segmentation scheme has exhibited high resource saving of 43% and 71% for a small degradation in a frame rate of 3% and 14%, respectively.

Electrical Tomography Hardware Systems for Real-Time Applications: a Review

“…The system utilized only 11% of logic gates and 15% of memory, which may make it adequate to host other more complex ET algorithms with a larger number of image pixels. In a similar extension of this work in [67], the complete image reconstruction was implemented on an FPGA (Cyclone-V) SoC platform, and the measurement data was acquired wirelessly. The iterative LBP method was used for reconstruction, and a segmentbased matrix-vector architecture was proposed, where the large matrix multiplications were carried out in smaller parallel segments.…”