In-FPGA instrumentation framework for openCL-based designs

ABSTRACT: The productivity achieved when developing applications on high-performance reconfigurable heterogeneous computing (HPRHC) systems is increased by using the Open Computing Language (OpenCL). However, the hardware produced by OpenCL compilers in field-programmable gate arrays (FPGAs) can result in severe performance bottlenecks that are challenging to solve. The problem is compounded by the fact that the generated netlist details are disorganized, making them mostly unreadable and only partially visible to designers. This paper proposes an in-FPGA instrumentation method and a new framework for extracting the FPGA-cycle-accurate timing performances of OpenCL-based designs. The results clearly show that the chosen execution model for OpenCL-based designs strongly affects the timing performance when it is not properly implemented. Our framework is implemented on an HPRHC platform that contains a CPU and two Arria10 FPGAs, and it is evaluated with a wide variety of benchmarks with different complexities. After testing on the reported benchmarks, the average logic overhead for one inserted instrument is 0.2 % of the total amount of adaptive look-up tables (ALUTs) and 0.1 % of the total registers in an FPGA. This resource utilization is between 1.5 and six times lower than those reported in the best previously published works. The scalability of the framework is also evaluated by inserting up to 50 instruments. The experimental results show that the average logic utilization per instrument is 0.19 % of the ALUTs and 0.17 % of the registers in the FPGA when 50 instruments are inserted

Tuning the Computational Effort: An Adaptive Accuracy-aware Approach Across System Layers

This thesis introduces a novel methodology to realize accuracy-aware systems, which will help designers integrate accuracy awareness into their systems. It proposes an adaptive accuracy-aware approach across system layers that addresses current challenges in that domain, combining and tuning accuracy-aware methods on different system layers. To widen the scope of accuracy-aware computing including approximate computing for other domains, this thesis presents innovative accuracy-aware methods and techniques for different system layers. The required tuning of the accuracy-aware methods is integrated into a configuration layer that tunes the available knobs of the accuracy-aware methods integrated into a system

NFComms: A synchronous communication framework for the CPU-NFP heterogeneous system

This work explores the viability of using a Network Flow Processor (NFP), developed by Netronome, as a coprocessor for the construction of a CPU-NFP heterogeneous platform in the domain of general processing. When considering heterogeneous platforms involving architectures like the NFP, the communication framework provided is typically represented as virtual network interfaces and is thus not suitable for generic communication. To enable a CPU-NFP heterogeneous platform for use in the domain of general computing, a suitable generic communication framework is required. A feasibility study for a suitable communication medium between the two candidate architectures showed that a generic framework that conforms to the mechanisms dictated by Communicating Sequential Processes is achievable. The resulting NFComms framework, which facilitates inter- and intra-architecture communication through the use of synchronous message passing, supports up to 16 unidirectional channels and includes queuing mechanisms for transparently supporting concurrent streams exceeding the channel count. The framework has a minimum latency of between 15.5 μs and 18 μs per synchronous transaction and can sustain a peak throughput of up to 30 Gbit/s. The framework also supports a runtime for interacting with the Go programming language, allowing user-space processes to subscribe channels to the framework for interacting with processes executing on the NFP. The viability of utilising a heterogeneous CPU-NFP system for use in the domain of general and network computing was explored by introducing a set of problems or applications spanning general computing, and network processing. These were implemented on the heterogeneous architecture and benchmarked against equivalent CPU-only and CPU/GPU solutions. The results recorded were used to form an opinion on the viability of using an NFP for general processing. It is the author’s opinion that, beyond very specific use cases, it appears that the NFP-400 is not currently a viable solution as a coprocessor in the field of general computing. This does not mean that the proposed framework or the concept of a heterogeneous CPU-NFP system should be discarded as such a system does have acceptable use in the fields of network and stream processing. Additionally, when comparing the recorded limitations to those seen during the early stages of general purpose GPU development, it is clear that general processing on the NFP is currently in a similar state

Recent Advances in Embedded Computing, Intelligence and Applications

The latest proliferation of Internet of Things deployments and edge computing combined with artificial intelligence has led to new exciting application scenarios, where embedded digital devices are essential enablers. Moreover, new powerful and efficient devices are appearing to cope with workloads formerly reserved for the cloud, such as deep learning. These devices allow processing close to where data are generated, avoiding bottlenecks due to communication limitations. The efficient integration of hardware, software and artificial intelligence capabilities deployed in real sensing contexts empowers the edge intelligence paradigm, which will ultimately contribute to the fostering of the offloading processing functionalities to the edge. In this Special Issue, researchers have contributed nine peer-reviewed papers covering a wide range of topics in the area of edge intelligence. Among them are hardware-accelerated implementations of deep neural networks, IoT platforms for extreme edge computing, neuro-evolvable and neuromorphic machine learning, and embedded recommender systems