arXiv subjects

William Andrew Simon

Publications and source records attributed to William Andrew Simon.

5 recordsLinked to original sources

arXiv2026

On Design Principles for Efficient Heterogeneous DRAM-PIM-GPU Systems

Heterogeneous DRAM-based processing-in-memory (PIM)-GPU systems promise significant efficiency gains for decode-phase large language model (LLM) inference, particularly in long-output generation, yet current design practices overlook critical factors that determine real-world performance. Through systematic evaluation of diverse architectures and workloads (OPT-7B/70B, Mamba2-2.7B/70B), we reveal three fundamental design principles: (i) static power consumption (DRAM leakage, refresh, and GPU idle power) can dominate the efficiency calculus, causing dynamic-only models to overestimate tokens/s/W by up to 3.85X for realistic deployments (Mamba2-2.7B, batch size 1, 128 input tokens, and 2,048 output tokens); (ii) decoding performance is monotonically non-decreasing with channel count across all evaluated models and workloads, generally plateauing at high channel counts for low-batch workloads; under a fixed-capacity sweep, all models instead share a common near-optimal hierarchy configuration, with substantially larger misconfiguration penalties for attention-based models; (iii) workload mapping strategies provide bounded improvements (up to 14.0%/17.4% kernel-level latency/energy reduction, up to 5.6% end-to-end gain) and are not primary bottlenecks. Significant efficiency gains require system-wide co-optimization. These principles provide design-space guidance for architects designing the next generation of memory-accelerated LLM systems.

cs.AR↗

arXiv2026

Processing-in-memory for genomics workloads

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, and Participatory (termed 'P4') medicine. All genomic data are currently processed in energy-hungry computer clusters and centers, necessitating data transfer, consuming substantial energy, and wasting valuable time. Therefore, there is a need for fast, energy-efficient, and cost-efficient technologies that enable genomics research without requiring data centers and cloud platforms. We recently launched the BioPIM Project to leverage emerging processing-in-memory (PIM) technologies to enable energy- and cost-efficient analysis of bioinformatics workloads. The BioPIM Project focuses on co-designing algorithms and data structures commonly used in genomics with several PIM architectures to achieve the highest cost, energy, and time savings.

q-bio.GN↗

arXiv2025

Enhancing Downstream Analysis in Genome Sequencing: Species Classification While Basecalling

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 science. This paper introduces a novel method to profile signals coming from sequencing devices in parallel with determining their nucleotide sequences, a process known as basecalling, via a multi-objective deep neural network for simultaneous basecalling and multi-class genome classification. We introduce a new loss strategy where losses for basecalling and classification are back-propagated separately, with model weights combined for the shared layers, and a pre-configured ranking strategy allowing top-K species accuracy, giving users flexibility to choose between higher accuracy or higher speed at identifying the species. We achieve state-of-the-art basecalling accuracies, while classification accuracies meet and exceed the results of state-of-the-art binary classifiers, attaining an average of 92.5%/98.9% accuracy at identifying the top-1/3 species among a total of 17 genomes in the Wick bacterial dataset. The work presented here has implications for future studies in metagenomic profiling by accelerating the bottleneck step of matching the DNA sequence to the correct genome.

q-bio.GN↗

arXiv2025

CiMBA: Accelerating Genome Sequencing through On-Device Basecalling via Compute-in-Memory

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 challenges. First, the creation of up to 0.5 GB of data per minute imposes substantial communication and storage overheads. Second, the sequencing pipeline is bottlenecked at the basecalling step, consuming >40% of genome analysis time. A range of proposals have attempted to address these challenges, with limited success. We propose to address these challenges with a Compute-in-Memory Basecalling Accelerator (CiMBA), the first embedded ($\sim25$mm$^2$) accelerator capable of real-time, on-device basecalling, coupled with AnaLog (AL)-Dorado, a new family of analog focused basecalling DNNs. Our resulting hardware/software co-design greatly reduces data communication overhead, is capable of a throughput of 4.77 million bases per second, 24x that required for real-time operation, and achieves 17x/27x power/area efficiency over the best prior basecalling embedded accelerator while maintaining a high accuracy comparable to state-of-the-art software basecallers.

cs.AR↗

arXiv2022

HDTorch: Accelerating Hyperdimensional Computing with GP-GPUs for Design Space Exploration

HyperDimensional Computing (HDC) as a machine learning paradigm is highly interesting for applications involving continuous, semi-supervised learning for long-term monitoring. However, its accuracy is not yet on par with other Machine Learning (ML) approaches. Frameworks enabling fast design space exploration to find practical algorithms are necessary to make HD computing competitive with other ML techniques. To this end, we introduce HDTorch, an open-source, PyTorch-based HDC library with CUDA extensions for hypervector operations. We demonstrate HDTorch's utility by analyzing four HDC benchmark datasets in terms of accuracy, runtime, and memory consumption, utilizing both classical and online HD training methodologies. We demonstrate average (training)/inference speedups of (111x/68x)/87x for classical/online HD, respectively. Moreover, we analyze the effects of varying hyperparameters on runtime and accuracy. Finally, we demonstrate how HDTorch enables exploration of HDC strategies applied to large, real-world datasets. We perform the first-ever HD training and inference analysis of the entirety of the CHB-MIT EEG epilepsy database. Results show that the typical approach of training on a subset of the data does not necessarily generalize to the entire dataset, an important factor when developing future HD models for medical wearable devices.

cs.LG↗