Improving Design Efficiency for Large-Scale Heterogeneous Circuits

Despite increases in logic density, many Big Data applications must still be partitioned across multiple computing devices in order to meet their strict performance requirements. Among the most demanding of these applications is high-energy physics (HEP), which uses complex computing systems consisting of thousands of FPGAs and ASICs to process the sensor data created by experiments at particles accelerators such as the Large Hadron Collider (LHC). Designing such computing systems is challenging due to the scale of the systems, the exceptionally high-throughput and low-latency performance constraints that necessitate application-specific hardware implementations, the requirement that algorithms are efficiently partitioned across many devices, and the possible need to update the implemented algorithms during the lifetime of the system.;In this work, we describe our research to develop flexible architectures for implementing such large-scale circuits on FPGAs. In particular, this work is motivated by (but not limited in scope to) high-energy physics algorithms for the Compact Muon Solenoid (CMS) experiment at the LHC. To make efficient use of logic resources in multi-FPGA systems, we introduce Multi-Personality Partitioning, a novel form of the graph partitioning problem, and present partitioning algorithms that can significantly improve resource utilization on heterogeneous devices while also reducing inter-chip connections. To reduce the high communication costs of Big Data applications, we also introduce Information-Aware Partitioning, a partitioning method that analyzes the data content of application-specific circuits, characterizes their entropy, and selects circuit partitions that enable efficient compression of data between chips. We employ our information-aware partitioning method to improve the performance of the hardware validation platform for evaluating new algorithms for the CMS experiment.;Together, these research efforts help to improve the efficiency and decrease the cost of the developing large-scale, heterogeneous circuits needed to enable large-scale application in high-energy physics and other important areas.