Over the last decade, the advent of high-throughput sequencing techniques brought an exponential growth in biosequence database sizes. With increased throughput demand and popularity of computational biology tools, reducing time-to-solution during computational analysis has become a significant challenge in the path to scientific discovery.
Conventional computer architecture is proven to be inefficient for computational biology and bioinformatics tasks. For example, aligning even several hundred DNA or protein sequences using progressive multiple alignment tools consumes several CPU hours on high performance computer. Hence, computational biology and bioinformatics rely on hardware accelerators to allow processing to keep up with the increasing amount of data generated from biology applications.
In a typical application, dominant portion of the runtime is spent in a small number of computational kernels, making it an excellent target for hardware acceleration. The combination of increasingly large datasets and high performance computing requirements make computational biology prime candidate to benefit from accelerator architecture research. Potential directions include 3D integration, near-data processing, automata processing, associative processing and reconfigurable architectures.
Short bio: Leonid received his MSc and PhD in Electrical Engineering from the Technion. After graduating, he co-founded VisionTech where he co-designed a single chip MPEG2 codec. Following VisionTech’s acquisition by Broadcom, he co-founded Horizon Semiconductors where he co-designed a Set Top Box on chip for cable and satellite TV.
Leonid is a postdoc fellow in Electrical Engineering in the Technion. He co-authored a number of patents and research papers on SoC and ASIC. His research interests include non von Neumann computer architectures and processing in memory
Roman received his BSC and MSc from the faculty of Electrical Engineering, Technion, Israel in 2009 and 2015, respectively. He is now a PhD candidate in the same faculty under the supervision of Prof. Ran Ginosar.
Roman's research interests are parallel computer architectures, in-data processing, accelerators for machine learning and big data, and novel computer architectures for bioinformatics applications.