Replication for Logic Bipartitioning

Logic replication, the duplication of logic in order to limit communication between partitions, is an effective part of a complete partitioning solution. In this paper we seek a better understanding of the important issues in logic replication. By developing new optimizations to existing algorithms we are able to significantly improve the quality of these techniques, achieving up to 12.5 % better results than the best existing replication techniques. When integrated into our already state-of-the-art partitioner, we improve overall cutsizes by 37.8%, while requiring the duplication of at most 7 % of the logic.

Runtime and quality tradeoffs in FPGA placement and routing

Abstract Many applications of FPGAs, especially logic emulation and custom computing, require the quick placement and routing of circuit designs. In these applications, the advantages FPGA-based systems have over software simulation are diminished by the long run-times of current CAD software used to map the circuit onto FPGAs. To improve the run-time advantage of FPGA systems, users may be willing to trade some mapping quality for a reduction in CAD tool runtimes. In this paper, we seek to establish how much quality degradation is necessary to achieve a given runtime improvement. For this purpose, we implemented and investigated numerous placement and routing algorithms for FPGAs. We also developed new tradeoff-oriented algorithms, where a tuning parameter can be used to control this quality vs. runtime tradeoff. We show how different algorithms can achieve different points within this tradeoff spectrum, as well as how a single algorithm can be tuned to form a curve in the spectrum. We demonstrate that the algorithms vary widely in their tradeoffs, with the fastest algorithm being 8x faster than the slowest, and the highest quality algorithm being 5x better than the least quality algorithm. Compared to the commercial Xilinx CAD tools, we can achieve a 3x speed-up by allowing 1.27x degradation in quality, and a factor of 1.6x quality improvement with 2x slowdown

A Scalable High-Bandwidth Architecture for Lossless Compression on FPGAs

Abstract-Data compression techniques have been the subject of intense study over the past several decades due to exponential increases in the quantity of data stored and transmitted by computer systems. Compression algorithms are traditionally forced to make tradeoffs between throughput and compression quality (the ratio of original file size to compressed file size). FPGAs represent a compelling substrate for streaming applications such as data compression thanks to their capacity for deep pipelines and custom caching solutions. Unfortunately, data hazards in compression algorithms such as LZ77 inhibit the creation of deep pipelines without sacrificing some amount of compression quality. In this work we detail a scalable fully pipelined FPGA accelerator that performs LZ77 compression and static Huffman encoding at rates up to 5.6 GB/s. Furthermore, we explore tradeoffs between compression quality and FPGA area that allow the same throughput at a fraction of the logic utilization in exchange for moderate reductions in compression quality. Compared to recent FPGA compression studies, our emphasis on scalability gives our accelerator a 3.0x advantage in resource utilization at equivalent throughput and compression ratio

Shallow waters: social science research in South Africa's marine environment

This paper provides an overview of social science research in the marine environment of South Africa for the period 1994–2012. A bibliography based on a review of relevant literature and social science projects funded under the SEAChange programme of the South African Network for Coastal and Oceanic Research (SANCOR) was used to identify nine main themes that capture the knowledge generated in the marine social science field. Within these themes, a wide diversity of topics has been explored, covering a wide geographic area. The review suggests that there has been a steady increase in social science research activities and outputs over the past 18 years, with a marked increase in postgraduate dissertations in this field. The SEAChange programme has contributed to enhancing understanding of certain issues and social interactions in the marine environment but this work is limited. Furthermore, there has been limited dissemination of these research results amongst the broader marine science community and incorporation of this information into policy and management decisions has also been limited. However, marine scientists are increasingly recognising the importance of taking a more holistic and integrated approach to management, and are encouraging further social science research, as well as interdisciplinary research across the natural and social sciences. Possible reasons for the lack of communication and coordination amongst natural and social scientists, as well as the limited uptake of research results in policy and management decisions, are discussed and recommendations are proposed.Web of Scienc

FPGA-accelerated machine learning inference as a service for particle physics computing

New heterogeneous computing paradigms on dedicated hardware with increased parallelization, such as Field Programmable Gate Arrays (FPGAs), offer exciting solutions with large potential gains. The growing applications of machine learning algorithms in particle physics for simulation, reconstruction, and analysis are naturally deployed on such platforms. We demonstrate that the acceleration of machine learning inference as a web service represents a heterogeneous computing solution for particle physics experiments that potentially requires minimal modification to the current computing model. As examples, we retrain the ResNet-50 convolutional neural network to demonstrate state-of-the-art performance for top quark jet tagging at the LHC and apply a ResNet-50 model with transfer learning for neutrino event classification. Using Project Brainwave by Microsoft to accelerate the ResNet-50 image classification model, we achieve average inference times of 60 (10) milliseconds with our experimental physics software framework using Brainwave as a cloud (edge or on-premises) service, representing an improvement by a factor of approximately 30 (175) in model inference latency over traditional CPU inference in current experimental hardware. A single FPGA service accessed by many CPUs achieves a throughput of 600--700 inferences per second using an image batch of one, comparable to large batch-size GPU throughput and significantly better than small batch-size GPU throughput. Deployed as an edge or cloud service for the particle physics computing model, coprocessor accelerators can have a higher duty cycle and are potentially much more cost-effective.Comment: 16 pages, 14 figures, 2 table

Low Latency Edge Classification GNN for Particle Trajectory Tracking on FPGAs

In-time particle trajectory reconstruction in the Large Hadron Collider is challenging due to the high collision rate and numerous particle hits. Using GNN (Graph Neural Network) on FPGA has enabled superior accuracy with flexible trajectory classification. However, existing GNN architectures have inefficient resource usage and insufficient parallelism for edge classification. This paper introduces a resource-efficient GNN architecture on FPGAs for low latency particle tracking. The modular architecture facilitates design scalability to support large graphs. Leveraging the geometric properties of hit detectors further reduces graph complexity and resource usage. Our results on Xilinx UltraScale+ VU9P demonstrate 1625x and 1574x performance improvement over CPU and GPU respectively

Differences in the emotional and practical experiences of exclusively breastfeeding and combination feeding mothers

The majority of research examining the barriers to breastfeeding focuses on the physical challenges faced by mothers rather than the risks of encountering negative emotional and practical feeding experiences. We aimed to quantify the emotional and practical experiences of the overall sample of breastfeeding mothers and identify the differences in the emotional and practical experiences of exclusively breastfeeding mothers and combination feeding mothers, by feeding type and intention. Eight hundred forty‐five mothers with infants up to 26 weeks of age and who had initiated breastfeeding were recruited through relevant social media via advertisements providing a link to an online survey. Predictors of emotional experiences included guilt, stigma, satisfaction with feeding method, and the need to defend themselves due to infant feeding choices. Practical predictors included perceived support from health professionals, main sources of infant feeding information, and respect from their everyday environment, workplace, and when breastfeeding in public. Current feeding type and prenatal feeding intention. In the overall sample, 15% of the mothers reported feeling guilty, 38% stigmatized, and 55% felt the need to defend their feeding choice. Binary logit models revealed that guilt and dissatisfaction were directly associated with feeding type, being higher when supplementing with formula. No associations with feeding intention were identified. This study demonstrates a link between current breastfeeding promotion strategies and the emotional state of breastfeeding mothers who supplement with formula to any extent. To minimize the negative impact on maternal well‐being, it is important that future recommendations recognize the challenges that exclusive breastfeeding brings and provide a more balanced and realistic target for mothers