OpenPARF: An Open-Source Placement and Routing Framework for Large-Scale Heterogeneous FPGAs with Deep Learning Toolkit

This paper proposes OpenPARF, an open-source placement and routing framework for large-scale FPGA designs. OpenPARF is implemented with the deep learning toolkit PyTorch and supports massive parallelization on GPU. The framework proposes a novel asymmetric multi-electrostatic field system to solve FPGA placement. It considers fine-grained routing resources inside configurable logic blocks (CLBs) for FPGA routing and supports large-scale irregular routing resource graphs. Experimental results on ISPD 2016 and ISPD 2017 FPGA contest benchmarks and industrial benchmarks demonstrate that OpenPARF can achieve 0.4-12.7% improvement in routed wirelength and more than $2\times$ speedup in placement. We believe that OpenPARF can pave the road for developing FPGA physical design engines and stimulate further research on related topics

DREAMPlaceFPGA-MP: An Open-Source GPU-Accelerated Macro Placer for Modern FPGAs with Cascade Shapes and Region Constraints

FPGA macro placement plays a pivotal role in routability and timing closer to the modern FPGA physical design flow. In modern FPGAs, macros could be subject to complex cascade shape constraints requiring instances to be placed in consecutive sites. In addition, in real-world FPGA macro placement scenarios, designs could have various region constraints that specify boundaries within which certain design instances and macros should be placed. In this work, we present DREAMPlaceFPGA-MP, an open-source GPU-accelerated FPGA macro-placer that efficiently generates legal placements for macros while honoring cascade shape requirements and region constraints. Treating multiple macros in a cascade shape as a large single instance and restricting instances to their respective regions, DREAMPlaceFPGA-MP obtains roughly legal placements. The macros are legalized in multiple steps to efficiently handle cascade shapes and region constraints. Our experimental results demonstrate that DREAMPlaceFPGA-MP is among the top contestants of the MLCAD 2023 FPGA Macro-Placement Contest

Smart technologies for effective reconfiguration: the FASTER approach

Current and future computing systems increasingly require that their functionality stays flexible after the system is operational, in order to cope with changing user requirements and improvements in system features, i.e. changing protocols and data-coding standards, evolving demands for support of different user applications, and newly emerging applications in communication, computing and consumer electronics. Therefore, extending the functionality and the lifetime of products requires the addition of new functionality to track and satisfy the customers needs and market and technology trends. Many contemporary products along with the software part incorporate hardware accelerators for reasons of performance and power efficiency. While adaptivity of software is straightforward, adaptation of the hardware to changing requirements constitutes a challenging problem requiring delicate solutions. The FASTER (Facilitating Analysis and Synthesis Technologies for Effective Reconfiguration) project aims at introducing a complete methodology to allow designers to easily implement a system specification on a platform which includes a general purpose processor combined with multiple accelerators running on an FPGA, taking as input a high-level description and fully exploiting, both at design time and at run time, the capabilities of partial dynamic reconfiguration. The goal is that for selected application domains, the FASTER toolchain will be able to reduce the design and verification time of complex reconfigurable systems providing additional novel verification features that are not available in existing tool flows

Optimising and evaluating designs for reconfigurable hardware

Growing demand for computational performance, and the rising cost for chip design and manufacturing make reconfigurable hardware increasingly attractive for digital system implementation. Reconfigurable hardware, such as field-programmable gate arrays (FPGAs), can deliver performance through parallelism while also providing flexibility to enable application builders to reconfigure them. However, reconfigurable systems, particularly those involving run-time reconfiguration, are often developed in an ad-hoc manner. Such an approach usually results in low designer productivity and can lead to inefficient designs. This thesis covers three main achievements that address this situation. The first achievement is a model that captures design parameters of reconfigurable hardware and performance parameters of a given application domain. This model supports optimisations for several design metrics such as performance, area, and power consumption. The second achievement is a technique that enhances the relocatability of bitstreams for reconfigurable devices, taking into account heterogeneous resources. This method increases the flexibility of modules represented by these bitstreams while reducing configuration storage size and design compilation time. The third achievement is a technique to characterise the power consumption of FPGAs in different activity modes. This technique includes the evaluation of standby power and dedicated low-power modes, which are crucial in meeting the requirements for battery-based mobile devices

The IPS fidelity scale as a guideline to implement Supported Employment

info:eu-repo/semantics/publishe

Hybrid FPGA: Architecture and Interface

Hybrid FPGAs (Field Programmable Gate Arrays) are composed of general-purpose logic resources with different granularities, together with domain-specific coarse-grained units. This thesis proposes a novel hybrid FPGA architecture with embedded coarse-grained Floating Point Units (FPUs) to improve the floating point capability of FPGAs. Based on the proposed hybrid FPGA architecture, we examine three aspects to optimise the speed and area for domain-specific applications. First, we examine the interface between large coarse-grained embedded blocks (EBs) and fine-grained elements in hybrid FPGAs. The interface includes parameters for varying: (1) aspect ratio of EBs, (2) position of the EBs in the FPGA, (3) I/O pins arrangement of EBs, (4) interconnect flexibility of EBs, and (5) location of additional embedded elements such as memory. Second, we examine the interconnect structure for hybrid FPGAs. We investigate how large and highdensity EBs affect the routing demand for hybrid FPGAs over a set of domain-specific applications. We then propose three routing optimisation methods to meet the additional routing demand introduced by large EBs: (1) identifying the best separation distance between EBs, (2) adding routing switches on EBs to increase routing flexibility, and (3) introducing wider channel width near the edge of EBs. We study and compare the trade-offs in delay, area and routability of these three optimisation methods. Finally, we employ common subgraph extraction to determine the number of floating point adders/subtractors, multipliers and wordblocks in the FPUs. The wordblocks include registers and can implement fixed point operations. We study the area, speed and utilisation trade-offs of the selected FPU subgraphs in a set of floating point benchmark circuits. We develop an optimised coarse-grained FPU, taking into account both architectural and system-level issues. Furthermore, we investigate the trade-offs between granularities and performance by composing small FPUs into a large FPU. The results of this thesis would help design a domain-specific hybrid FPGA to meet user requirements, by optimising for speed, area or a combination of speed and area

Optimizing Scrubbing by Netlist Analysis for FPGA Configuration Bit Classification and Floorplanning

Existing scrubbing techniques for SEU mitigation on FPGAs do not guarantee an error-free operation after SEU recovering if the affected configuration bits do belong to feedback loops of the implemented circuits. In this paper, we a) provide a netlist-based circuit analysis technique to distinguish so-called critical configuration bits from essential bits in order to identify configuration bits which will need also state-restoring actions after a recovered SEU and which not. Furthermore, b) an alternative classification approach using fault injection is developed in order to compare both classification techniques. Moreover, c) we will propose a floorplanning approach for reducing the effective number of scrubbed frames and d), experimental results will give evidence that our optimization methodology not only allows to detect errors earlier but also to minimize the Mean-Time-To-Repair (MTTR) of a circuit considerably. In particular, we show that by using our approach, the MTTR for datapath-intensive circuits can be reduced by up to 48.5% in comparison to standard approaches