Real-time data analysis for medical diagnosis using FPGA-accelerated neural networks.

Background: Real-time analysis of patient data during medical procedures can provide vital diagnostic feedback that significantly improves chances of success. With sensors becoming increasingly fast, frameworks such as Deep Neural Networks are required to perform calculations within the strict timing constraints for real-time operation. However, traditional computing platforms responsible for running these algorithms incur a large overhead due to communication protocols, memory accesses, and static (often generic) architectures.

GPU Optimizations for a Production Molecular Docking Code.

Modeling molecular docking is critical to both understanding life processes and designing new drugs. In previous work we created the first published GPU-accelerated docking code (PIPER) which achieved a roughly 5× speed-up over a contemporaneous 4 core CPU. Advances in GPU architecture and in the CPU code, however, have since reduced this relalative performance by a factor of 10.

3D FFTs on a Single FPGA.

The 3D FFT is critical in many physical simulations and image processing applications. On FPGAs, however, the 3D FFT was thought to be inefficient relative to other methods such as convolution-based implementations of multi-grid. We find the opposite: a simple design, operating at a conservative frequency, takes 4s for 16, 21s for 32, and 215s for 64 single precision data points.

Parallel Discrete Molecular Dynamics Simulation With Speculation and In-Order Commitment.

Discrete molecular dynamics simulation (DMD) uses simplified and discretized models enabling simulations to advance by event rather than by timestep. DMD is an instance of discrete event simulation and so is difficult to scale: even in this multi-core era, all reported DMD codes are serial. In this paper we discuss the inherent difficulties of scaling DMD and present our method of parallelizing DMD through event-based decomposition.

Molecular Dynamics Simulations on High-Performance Reconfigurable Computing Systems.

The acceleration of molecular dynamics (MD) simulations using high-performance reconfigurable computing (HPRC) has been much studied. Given the intense competition from multicore and GPUs, there is now a question whether MD on HPRC can be competitive. We concentrate here on the MD kernel computation: determining the short-range force between particle pairs.

Explicit Design of FPGA-Based Coprocessors for Short-Range Force Computations in Molecular Dynamics Simulations.

FPGA-based acceleration of molecular dynamics simulations (MD) has been the subject of several recent studies. The short-range force computation, which dominates the execution time, is the primary focus. Here we combine: a high level of FPGA-specific design including cell lists, systematically determined interpolation and precision, handling of exclusion, and support for MD simulations of up to 256K particles.

Single Pass Streaming BLAST on FPGAs.

Approximate string matching is fundamental to bioinformatics and has been the subject of numerous FPGA acceleration studies. We address issues with respect to FPGA implementations of both BLAST- and dynamic-programming- (DP) based methods. Our primary contribution is a new algorithm for emulating the seeding and extension phases of BLAST.

Computing Models for FPGA-Based Accelerators.

Field-programmable gate arrays are widely considered as accelerators for compute-intensive applications. A critical phase of FPGA application development is finding and mapping to the appropriate computing model. FPGA computing enables models with highly flexible fine-grained parallelism and associative operations such as broadcast and collective response.

Families of FPGA-Based Accelerators for Approximate String Matching.

Dynamic programming for approximate string matching is a large family of different algorithms, which vary significantly in purpose, complexity, and hardware utilization. Many implementations have reported impressive speed-ups, but have typically been point solutions - highly specialized and addressing only one or a few of the many possible options. The problem to be solved is creating a hardware description that implements a broad range of behavioral options without losing efficiency due to feature bloat.

Achieving High Performance with FPGA-Based Computing.

Numerous application areas, including bioinformatics and computational biology, demand increasing amounts of processing capability. In many cases, the computation cores and data types are suited to field-programmable gate arrays. The challenge is identifying the design techniques that can extract high performance potential from the FPGA fabric.