HW-SW Implementation of a Decoupled FPU for ARM-based Cortex-M1 SoCs in FPGAs

Nowadays industrial monoprocessor and multipro- cessor systems make use of hardware floating-point units (FPUs) to provide software acceleration and better precision due to the necessity to compute complex software applications. This paper presents the design of an IEEE-754 compliant FPU, targeted to be used with ARM Cortex-M1 processor on FPGA SoCs. We face the design of an AMBA-based decoupled FPU in order to avoid changing of the Cortex-M1 ARMv6-M architecture and the ARM compiler, but as well to eventually share it among different processors in our Cortex-M1 MPSoC design. Our HW- SW implementation can be easily integrated to enable hardware- assisted floating-point operations transparently from the software application. This work reports synthesis results of our Cortex-M1 SoC architecture, as well as our FPU in Altera and Xilinx FPGAs, which exhibit competitive numbers compared to the equivalent Xilinx FPU IP core. Additionally, single and double precision tests have been performed under different scenarios showing best case speedups between 8.8x and 53.2x depending on the FP operation when are compared to FP software emulation libraries

QoS-Driven Reconfigurable Parallel Computing for NoC-Based Clustered MPSoCs

Reconfigurable parallel computing is required to provide high-performance embedded computing, hide hardware complexity, boost software development, and manage multiple workloads when multiple applications are running simultaneously on the emerging network-on-chip (NoC)-based multiprocessor systems-on-chip (MPSoCs) platforms. In these type of systems, the overall system performance may be affected due to congestion, and therefore parallel programming stacks must be assisted by quality-of-service (QoS) support to meet application requirements and to deal with application dynamism. In this paper, we present a hardware-software QoS-driven reconfigurable parallel computing framework, i.e., the NoC services, the runtime QoS middleware API and our ocMPI library and its tracing support which has been tailored for a distributed-shared memory ARM clustered NoC-based MPSoC platform. The experimental results show the efficiency of our software stack under a broad range of parallel kernels and benchmarks, in terms of low-latency interprocess communication, good application scalability, and most important, they demonstrate the ability to enable runtime reconfiguration to manage workloads in message-passing parallel applications