269 citations · 1.1k across the 57 of their papers we have counts for
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Accelerating Neural Network Inference with Processing-in-DRAM: From the Edge to the Cloud
Geraldo F. Oliveira, Juan Gómez-Luna, Saugata Ghose +2
Neural networks (NNs) are growing in importance and complexity. A neural network's performance (and energy efficiency) can be bound either by computation or memory resources. The p…
Methodologies, Workloads, and Tools for Processing-in-Memory: Enabling the Adoption of Data-Centric Architectures
Geraldo F. Oliveira, Juan Gómez-Luna, Saugata Ghose +1
The increasing prevalence and growing size of data in modern applications have led to high costs for computation in traditional processor-centric computing systems. Moving large vo…
SeGraM: A Universal Hardware Accelerator for Genomic Sequence-to-Graph and Sequence-to-Sequence Mapping
Damla Senol Cali, Konstantinos Kanellopoulos, Joel Lindegger +15
A critical step of genome sequence analysis is the mapping of sequenced DNA fragments (i.e., reads) collected from an individual to a known linear reference genome sequence (i.e.,…
Heterogeneous Data-Centric Architectures for Modern Data-Intensive Applications: Case Studies in Machine Learning and Databases
Geraldo F. Oliveira, Amirali Boroumand, Saugata Ghose +2
Today's computing systems require moving data back-and-forth between computing resources (e.g., CPUs, GPUs, accelerators) and off-chip main memory so that computation can take plac…
Enabling High-Performance and Energy-Efficient Hybrid Transactional/Analytical Databases with Hardware/Software Cooperation
Amirali Boroumand, Saugata Ghose, Geraldo F. Oliveira +1
A growth in data volume, combined with increasing demand for real-time analysis (using the most recent data), has resulted in the emergence of database systems that concurrently su…