How to accelerate genomic sequence alignment 4X using half an FPGA
Alexandre Cornu, Steven Derrien, and Dominique Lavenier, INRIA / IRISA, Rennes, France
7/5/2011 3:00 PM EDT
Designing FPGA-based accelerators is a difficult and time-consuming task that can be eased by High Level Synthesis Tools. To illustrate, we describe how a C-to-hardware methodology has been used to develop an efficient systolic array for the genomic sequence alignment problem. We also compare design performance with traditional HDL implementations.
FPGA gate density is doubling approximately every two years. Consequently, increasingly complex designs can be integrated into a single FPGA, which can now be considered as a high-power computing accelerator. However, pushing processing into such devices can lead to development time and design reliability issues. Recent efforts have helped FPGA-targeted application designers deal with large amounts of resources. In particular, Electronic System Level tools provide a higher level of abstraction than traditional HDL design flows. Several High Level Languages (HLL) for modeling complex systems, and corresponding High Level Synthesis (HLS) Tools to translate HLL-based designs into HDL synthesizable projects are available. Most of them are based on a subset of C/C++ [1] generally extended with specific types or I/O capabilities. Here we focus on the C to FPGA tool set [3] and its associated flow.
To read the full article, click here
Related Semiconductor IP
- Zigbee Transceiver PHY
- Data Flow Architecture IP
- AMBA SPI Controller MRAM Controller
- Ethernet MAC
- Protocol Bridges
Related Articles
- Using parallel FFT for multi-gigahertz FPGA signal processing
- Control an FPGA bus without using the processor
- FPGA design from scratch
- How to turn every FPGA LVDS pair into a complete SERDES solution
Latest Articles
- A Low-Latency ASIC Architecture for Real-Time Line Segment Detection
- BitFair: A 12nm Bit-Serial CNN Accelerator with Learnable Early Termination and Adaptive Bit Ordering for Ultra-Low-Power XR Vision
- A Flexible Sparsity-Aware FPGA Accelerator with Column-Wise Compression for Efficient CNN Inference
- Reducing Instruction-Fetch Energy in RISC-V for Embedded AI Processing via Dynamic and Static Loop Caching
- SPARC: Automated Root-Cause Analysis of Pre-Silicon Power Side-Channel Leakage in the Processor Design Flow