On-chip FPGA Research Articles

An Approach to the Systematic Characterization of Multitask Accelerated CNN Inference in Edge MPSoCs

Deep Learning is ubiquitous today and is increasingly moving from the cloud down to the edge of networked infrastructures, where it enables embedded applications to perform complex inference tasks close to the data sources, reducing long-distance data movement and alleviating the need for a powerful cloud infrastructure. Edge-class multi-processor system on chip (MPSoC) devices featuring an on-chip FPGA fabric offer key advantages for Deep Learning inference tasks, especially for complex applications where multiple models may be run concurrently in the same platform. In this work, we propose an approach and a practical framework for the systematic characterization of multithreaded Deep Learning inference on edge FPGA MPSoCs. We instantiate the framework into a real-world MPSoC platform, targeting Xilinx Vitis-AI as a representative example of a commercial Deep Learning acceleration toolkit for edge environments. We design a comprehensive experimental campaign and apply it to the platform for several convolutional neural networks, each trained on three different datasets. We show that our approach can be used for both hardware- and software-level analysis of a target system. Among other findings, the analysis revealed a suboptimal behavior of the underlying toolkit runtime, involving the utilization of the accelerator cores and the uneven software latency of the support library, influenced by the shapes of the input tensors.

Voltage Sensor Implementations for Remote Power Attacks on FPGAs

This article presents a study of two types of on-chip FPGA voltage sensors based on ring oscillators (ROs) and time-to-digital converter (TDCs), respectively. It has previously been shown that these sensors are often used to extract side-channel information from FPGAs without physical access. The performance of the sensors is evaluated in the presence of circuits that deliberately waste power, resulting in localized voltage drops. The effects of FPGA power supply features and sensor sensitivity in detecting voltage drops in an FPGA power distribution network (PDN) are evaluated for Xilinx Artix-7, Zynq 7000, and Zynq UltraScale+ FPGAs. We show that both sensor types are able to detect supply voltage drops, and that their measurements are consistent with each other. Our findings show that TDC-based sensors are more sensitive and can detect voltage drops that are shorter in duration, while RO sensors are easier to implement because calibration is not required. Furthermore, we present a new time-interleaved TDC design that sweeps the sensor phase. The new sensor generates data that can reconstruct voltage transients on the order of tens of picoseconds.

Autonomous hardware/software partitioning and voltage/frequency scaling for low-power embedded systems

Warp processing is a recent computing technology capable of autonomously partitioning the critical kernels within an executing software application to hardware circuits implemented within an on-chip FPGA. While previous performance-driven warp processing has been shown to provide significant performance improvements over software only execution, the dynamic performance improvement of warp processors may be lost for certain application domains, such as real-time systems. Alternatively, as power consumption continue to become a dominant design constraint, we present and thoroughly analyze a low-power warp processing methodology that leverages voltage and/or frequency scaling to substantially reduce power consumption without any performance degradation—all without requiring designer effort beyond the initial software development.

Scalability and Parallel Execution of Warp Processing: Dynamic Hardware/Software Partitioning

Warp processors are a novel architecture capable of autonomously optimizing an executing application by dynamically re-implementing critical kernels within the software as custom hardware circuits in an on-chip FPGA. Previous research on warp processing focused on low-power embedded systems, incorporating a low-end ARM processor as the main software execution resource. We provide a thorough analysis of the scalability of warp processing by evaluating several possible warp processor implementations, from low-power to high-performance, and by evaluating the potential for parallel execution of the partitioned software and hardware. We further demonstrate that even considering a high-performance 1 GHz embedded processor, warp processing provides the equivalent performance of a 2.4GHz processor. By further enabling parallel execution between the processes and FPGA, the parallel warp processor execution provides the equivalent performance of a 3.2 GHz processor.

RAM ALLOCATION ALGORITHM FOR VIDEO PROCESSING APPLICATIONS ON FPGA

This paper presents an algorithm for the allocation of on-chip FPGA Block RAMs for the implementation of Real-Time Video Processing Systems. The effectiveness of the algorithm is shown through the implementation of realistic image processing systems. The algorithm, which is based on a heuristic, seeks the most cost-effective way of allocating memory objects to the FPGA Block RAMs. The experimental results obtained, show that this algorithm generates results which are close to the theoretical optimum for most design cases.