898 citations · 1.5k across the 22 of their papers we have counts for
23 papers
On Design Principles for Efficient Heterogeneous DRAM-PIM-GPU Systems
Corey Lammie, Hadjer Benmeziane, William Andrew Simon +1
Heterogeneous DRAM-based processing-in-memory (PIM)-GPU systems promise significant efficiency gains for decode-phase large language model (LLM) inference, particularly in long-out…
Processing-in-memory for genomics workloads
William Andrew Simon, Leonid Yavits, Konstantina Koliogeorgi +12
Low-cost, high-throughput DNA and RNA sequencing (HTS) data is the backbone of the life sciences. Genome sequencing is now becoming a part of Predictive, Preventive, Personalized,…
CiMBA: Accelerating Genome Sequencing through On-Device Basecalling via Compute-in-Memory
William Andrew Simon, Irem Boybat, Riselda Kodra +8
As genome sequencing is finding utility in a wide variety of domains beyond the confines of traditional medical settings, its computational pipeline faces two significant challenge…
Enhancing Downstream Analysis in Genome Sequencing: Species Classification While Basecalling
Riselda Kodra, Hadjer Benmeziane, Irem Boybat +1
The ability to quickly and accurately identify microbial species in a sample, known as metagenomic profiling, is critical across various fields, from healthcare to environmental sc…
Efficient transformer adaptation for analog in-memory computing via low-rank adapters
Chen Li, Elena Ferro, Corey Lammie +3
Analog In-Memory Computing (AIMC) offers a promising solution to the von Neumann bottleneck. However, deploying transformer models on AIMC remains challenging due to their inherent…
A Precision-Optimized Fixed-Point Near-Memory Digital Processing Unit for Analog In-Memory Computing
Elena Ferro, Athanasios Vasilopoulos, Corey Lammie +4
Analog In-Memory Computing (AIMC) is an emerging technology for fast and energy-efficient Deep Learning (DL) inference. However, a certain amount of digital post-processing is requ…