Onur Mutlu | Professor, SAFARI Research Group
ETH Zurich
Onur Mutlu is a Professor of Computer Science at ETH Zurich. He is also a faculty member at Carnegie Mellon University, where he previously held the Strecker Early Career Professorship. His current broader research interests are in computer architecture, systems, hardware security, and bioinformatics. A variety of techniques he, along with his group and collaborators, has invented over the years have influenced industry and have been employed in commercial microprocessors and memory/storage systems. He obtained his PhD and MS in ECE from the University of Texas at Austin and BS degrees in Computer Engineering and Psychology from the University of Michigan, Ann Arbor. He started the Computer Architecture Group at Microsoft Research (2006-2009), and held various product and research positions at Intel Corporation, Advanced Micro Devices, VMware, and Google. He is an ACM Fellow "for contributions to computer architecture research, especially in memory systems", IEEE Fellow for "contributions to computer architecture research and practice", and an elected member of the Academy of Europe (Academia Europaea). For more information, please see https://people.inf.ethz.ch/omutlu
Appearances:
Future of Memory and Storage - Day 2 @ 08:35
SAGe: A Lightweight Algorithm-Architecture Co-Design for Mitigating the Data Preparation Bottleneck in Large-Scale Genomics
Given exponentially growing genomic data volumes, extensive efforts target accelerating genomic analysis. We identify a major bottleneck limiting genomic analysis accelerators: the data preparation bottleneck, where genomic sequence data is stored compressed and needs to be first decompressed and formatted before an accelerator can operate on it. To mitigate this bottleneck, we propose SAGe, an algorithm-architecture co-design for highly-compressed storage and high-performance access of large-scale genomic sequence data. The key challenge is to improve data preparation performance while maintaining high compression ratios (comparable to genomic compression algorithms) at low hardware cost. We address this challenge by leveraging key properties of genomic data to co-design (i) a lossless (de)compression algorithm, (ii) lightweight decompression hardware, (iii) storage data layout, and (iv) interface commands. SAGe integrates seamlessly with diverse genomic analysis accelerators, improving performance and energy efficiency of two state-of-the-art accelerators by 3.0x–32.1x and 13.0x–34.0x, respectively, compared to when relying on state-of-the-art SW and HW decompression tools.
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