SneakySnake: A Fast and Accurate Universal Genome Pre-Alignment Filter for CPUs, GPUs, and FPGAs

1 code implementation • 20 Oct 2019 • Mohammed Alser, Taha Shahroodi, Juan Gomez-Luna, Can Alkan, Onur Mutlu

The key idea of SneakySnake is to reduce the approximate string matching (ASM) problem to the single net routing (SNR) problem in VLSI chip layout.

GenASM: A High-Performance, Low-Power Approximate String Matching Acceleration Framework for Genome Sequence Analysis

2 code implementations • 16 Sep 2020 • Damla Senol Cali, Gurpreet S. Kalsi, Zülal Bingöl, Can Firtina, Lavanya Subramanian, Jeremie S. Kim, Rachata Ausavarungnirun, Mohammed Alser, Juan Gomez-Luna, Amirali Boroumand, Anant Nori, Allison Scibisz, Sreenivas Subramoney, Can Alkan, Saugata Ghose, Onur Mutlu

Unfortunately, it is currently bottlenecked by the computational power and memory bandwidth limitations of existing systems, as many of the steps in genome sequence analysis must process a large amount of data.

Hardware Architecture Genomics

EcoFlow: Efficient Convolutional Dataflows for Low-Power Neural Network Accelerators

1 code implementation • 4 Feb 2022 • Lois Orosa, Skanda Koppula, Yaman Umuroglu, Konstantinos Kanellopoulos, Juan Gomez-Luna, Michaela Blott, Kees Vissers, Onur Mutlu

We find that commonly-used low-power CNN inference accelerators based on spatial architectures are not optimized for both of these convolutional kernels.

Generative Adversarial Network Image Generation +2

Going From Molecules to Genomic Variations to Scientific Discovery: Intelligent Algorithms and Architectures for Intelligent Genome Analysis

1 code implementation • 16 May 2022 • Mohammed Alser, Joel Lindegger, Can Firtina, Nour Almadhoun, Haiyu Mao, Gagandeep Singh, Juan Gomez-Luna, Onur Mutlu

We hope that these efforts and the challenges we discuss provide a foundation for future work in making genome analysis more intelligent.