Related papers: GenASM: A High-Performance, Low-Power Approximate …
The GEneral Matrix Multiplication (GEMM) is one of the essential algorithms in scientific computing. Single-thread GEMM implementations are well-optimised with techniques like blocking and autotuning. However, due to the complexity of…
We present the Scalable Nucleotide Alignment Program (SNAP), a new short and long read aligner that is both more accurate (i.e., aligns more reads with fewer errors) and 10-100x faster than state-of-the-art tools such as BWA. Unlike recent…
Aligning millions of short DNA or RNA reads, of 75 to 250 base pairs each, to a reference genome is a significant computation problem in bioinformatics. We present a flexible and fast FPGA-based short read alignment tool. Our aligner makes…
Long Short-Term Memory (LSTM) neural networks have penetrated healthcare applications where real-time requirements and edge computing capabilities are essential. Gait analysis that detects abnormal steps to prevent patients from falling is…
Genome sequences contain hundreds of millions of DNA base pairs. Finding the degree of similarity between two genomes requires executing a compute-intensive dynamic programming algorithm, such as Smith-Waterman. Traditional von Neumann…
De novo assembly enables investigations of unknown genomes, paving the way for personalized medicine and disease management. However, it faces immense computational challenges arising from the excessive data volumes and algorithmic…
DNA pattern matching is essential for many widely used bioinformatics applications. Disease diagnosis is one of these applications, since analyzing changes in DNA sequences can increase our understanding of possible genetic diseases. The…
We discuss a high-performance and high-throughput hardware accelerator for probabilistic Spiking Neural Networks (SNNs) based on Generalized Linear Model (GLM) neurons, that uses binary STT-RAM devices as synapses and digital CMOS logic for…
DNA sequence alignment is an important workload in computational genomics. Reference-guided DNA assembly involves aligning many read sequences against candidate locations in a long reference genome. To reduce the computational load of this…
There are currently plenty of programs available for mapping short sequences (reads) to a genome. Most of them, however, including such popular and actively developed programs as Bowtie, BWA, TopHat and many others, are based on…
Recurrent Neural Networks (RNNs) are vital for sequential data processing. Long Short-Term Memory Autoencoders (LSTM-AEs) are particularly effective for unsupervised anomaly detection in time-series data. However, inherent sequential…
Sparse General Matrix-Matrix Multiplication (SpGEMM) is a fundamental operation in numerous scientific computing and data analytics applications, often bottlenecked by irregular memory access patterns. This paper presents Hash based…
Motivation: We introduce SneakySnake, a highly parallel and highly accurate pre-alignment filter that remarkably reduces the need for computationally costly sequence alignment. The key idea of SneakySnake is to reduce the approximate string…
Motivation: Computational methods are essential to extract actionable information from raw sequencing data, and to thus fulfill the promise of next-generation sequencing technology. Unfortunately, computational tools developed to call…
Approximate subgraph matching (ASM) is a task that determines the approximate presence of a given query graph in a large target graph. Being an NP-hard problem, ASM is critical in graph analysis with a myriad of applications ranging from…
Gene expression prediction, which predicts mRNA expression levels from DNA sequences, presents significant challenges. Previous works often focus on extending input sequence length to locate distal enhancers, which may influence target…
Raw signal genome analysis (RSGA) has emerged as a promising approach to enable real-time genome analysis by directly analyzing raw electrical signals. However, rapid advancements in sequencing technologies make it increasingly difficult…
Large-scale biobanks are being collected around the world in efforts to better understand human health and risk factors for disease. They often survey hundreds of thousands of individuals, combining questionnaires with clinical, genetic,…
Modeling biological sequences such as DNA, RNA, and proteins is crucial for understanding complex processes like gene regulation and protein synthesis. However, most current models either focus on a single type or treat multiple types of…
Dynamic programming (DP) algorithms, such as All-Pairs Shortest Path (APSP) and genomic sequence alignment, are fundamental to many scientific domains but are severely bottlenecked by data movement on conventional architectures. While…