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Found 56 papers, 32 papers with code
Analysis of Distributed Optimization Algorithms on a Real Processing-In-Memory System
Processor-centric architectures (e. g., CPU, GPU) commonly used for modern ML training workloads are limited by the data movement bottleneck, i. e., due to repeatedly accessing the training dataset.
Accelerating Graph Neural Networks on Real Processing-In-Memory Systems
Graph Neural Network (GNN) execution involves both compute-intensive and memory-intensive kernels, the latter dominates the total time, being significantly bottlenecked by data movement between memory and processors.
Demystifying Chains, Trees, and Graphs of Thoughts
Among these, prompt engineering coupled with structures has emerged as a promising paradigm, with designs such as Chain-of-Thought, Tree of Thoughts, or Graph of Thoughts, in which the overall LLM reasoning is guided by a structure such as a graph.
MetaTrinity: Enabling Fast Metagenomic Classification via Seed Counting and Edit Distance Approximation
This dual comparison positions MetaTrinity as a broadly applicable solution for metagenomic classification, combining advantages of both ends of the spectrum: speed and accuracy.
SequenceLab: A Comprehensive Benchmark of Computational Methods for Comparing Genomic Sequences
Comparing genomic sequences is one of the most fundamental computational steps in most genomic analyses.
RawAlign: Accurate, Fast, and Scalable Raw Nanopore Signal Mapping via Combining Seeding and Alignment
mean) while improving accuracy by 1. 35$\times$ (1. 34$\times$) in terms of F-1 score on average (geo.
RawHash2: Accurate and Fast Mapping of Raw Nanopore Signals using a Hash-based Seeding Mechanism
Summary: Raw nanopore signals can be analyzed while they are being generated, a process known as real-time analysis.
TransPimLib: A Library for Efficient Transcendental Functions on Processing-in-Memory Systems
In order to provide support for transcendental (and other hard-to-calculate) functions in general-purpose PIM systems, we present \emph{TransPimLib}, a library that provides CORDIC-based and LUT-based methods for trigonometric functions, hyperbolic functions, exponentiation, logarithm, square root, etc.
RawHash: Enabling Fast and Accurate Real-Time Analysis of Raw Nanopore Signals for Large Genomes
RawHash achieves an accurate hash-based similarity search via an effective quantization of the raw signals such that signals corresponding to the same DNA content have the same quantized value and, subsequently, the same hash value.
RedBit: An End-to-End Flexible Framework for Evaluating the Accuracy of Quantized CNNs
However, the large strides in accuracy obtained by CNNs have been derived from increasing the complexity of network topologies, which incurs sizeable performance and energy penalties in the training and inference of CNNs.
TargetCall: Eliminating the Wasted Computation in Basecalling via Pre-Basecalling Filtering
However, for many applications, the majority of reads do no match the reference genome of interest (i. e., target reference) and thus are discarded in later steps in the genomics pipeline, wasting the basecalling computation.
NEON: Enabling Efficient Support for Nonlinear Operations in Resistive RAM-based Neural Network Accelerators
Resistive Random-Access Memory (RRAM) is well-suited to accelerate neural network (NN) workloads as RRAM-based Processing-in-Memory (PIM) architectures natively support highly-parallel multiply-accumulate (MAC) operations that form the backbone of most NN workloads.
Accelerating Neural Network Inference with Processing-in-DRAM: From the Edge to the Cloud
Our analysis reveals that PIM greatly benefits memory-bound NNs: (1) UPMEM provides 23x the performance of a high-end GPU when the GPU requires memory oversubscription for a general matrix-vector multiplication kernel; (2) Mensa improves energy efficiency and throughput by 3. 0x and 3. 1x over the Google Edge TPU for 24 Google edge NN models; and (3) SIMDRAM outperforms a CPU/GPU by 16. 7x/1. 4x for three binary NNs.
Hermes: Accelerating Long-Latency Load Requests via Perceptron-Based Off-Chip Load Prediction
To this end, we propose a new technique called Hermes, whose key idea is to: 1) accurately predict which load requests might go off-chip, and 2) speculatively fetch the data required by the predicted off-chip loads directly from the main memory, while also concurrently accessing the cache hierarchy for such loads.
LEAPER: Fast and Accurate FPGA-based System Performance Prediction via Transfer Learning
The key idea of LEAPER is to transfer an ML-based performance and resource usage model trained for a low-end edge environment to a new, high-end cloud environment to provide fast and accurate predictions for accelerator implementation.
ApHMM: Accelerating Profile Hidden Markov Models for Fast and Energy-Efficient Genome Analysis
We identify an urgent need for a flexible, high-performance, and energy-efficient HW/SW co-design to address the major inefficiencies in the Baum-Welch algorithm for pHMMs.
An Experimental Evaluation of Machine Learning Training on a Real Processing-in-Memory System
Our K-Means clustering on PIM is $2. 8\times$ and $3. 2\times$ than state-of-the-art CPU and GPU versions, respectively.
COVIDHunter: COVID-19 pandemic wave prediction and mitigation via seasonality-aware modeling
We introduce COVIDHunter, a flexible and accurate COVID-19 outbreak simulation model that evaluates the current mitigation measures that are applied to a region, predicts COVID-19 statistics (the daily number of cases, hospitalizations, and deaths), and provides suggestions on what strength the upcoming mitigation measure should be.
Machine Learning Training on a Real Processing-in-Memory System
Our goal is to understand the potential of modern general-purpose PIM architectures to accelerate machine learning training.
Heterogeneous Data-Centric Architectures for Modern Data-Intensive Applications: Case Studies in Machine Learning and Databases
One promising execution paradigm that alleviates the data movement bottleneck in modern and emerging applications is processing-in-memory (PIM), where the cost of data movement to/from main memory is reduced by placing computation capabilities close to memory.
Going From Molecules to Genomic Variations to Scientific Discovery: Intelligent Algorithms and Architectures for Intelligent Genome Analysis
We hope that these efforts and the challenges we discuss provide a foundation for future work in making genome analysis more intelligent.
Sibyl: Adaptive and Extensible Data Placement in Hybrid Storage Systems Using Online Reinforcement Learning
We introduce Sibyl, the first technique that uses reinforcement learning for data placement in hybrid storage systems.
Packaging, containerization, and virtualization of computational omics methods: Advances, challenges, and opportunities
Omics software tools have reshaped the landscape of modern biology and become an essential component of biomedical research.
DeepSketch: A New Machine Learning-Based Reference Search Technique for Post-Deduplication Delta Compression
Data reduction in storage systems is becoming increasingly important as an effective solution to minimize the management cost of a data center.
EcoFlow: Efficient Convolutional Dataflows for Low-Power Neural Network Accelerators
We find that commonly-used low-power CNN inference accelerators based on spatial architectures are not optimized for both of these convolutional kernels.
FastRemap: A Tool for Quickly Remapping Reads between Genome Assemblies
Also available in Bioconda at: https://anaconda. org/bioconda/fastremap-bio.
GenShare: Sharing Accurate Differentially-Private Statistics for Genomic Datasets with Dependent Tuples
While sharing genomic data across researchers is an essential driver of advances in health and biomedical research, the sharing process is often infeasible due to data privacy concerns.
BLEND: A Fast, Memory-Efficient, and Accurate Mechanism to Find Fuzzy Seed Matches in Genome Analysis
We introduce BLEND, the first efficient and accurate mechanism that can identify both exact-matching and highly similar seeds with a single lookup of their hash values, called fuzzy seed matches.
Google Neural Network Models for Edge Devices: Analyzing and Mitigating Machine Learning Inference Bottlenecks
To understand how edge ML accelerators perform, we characterize the performance of a commercial Google Edge TPU, using 24 Google edge NN models (which span a wide range of NN model types) and analyzing each NN layer within each model.
Pythia: A Customizable Hardware Prefetching Framework Using Online Reinforcement Learning
In this paper, we make a case for designing a holistic prefetch algorithm that learns to prefetch using multiple different types of program context and system-level feedback information inherent to its design.
Energy-Efficient Mobile Robot Control via Run-time Monitoring of Environmental Complexity and Computing Workload
We propose an energy-efficient controller to minimize the energy consumption of a mobile robot by dynamically manipulating the mechanical and computational actuators of the robot.
GateKeeper-GPU: Fast and Accurate Pre-Alignment Filtering in Short Read Mapping
At the last step of short read mapping, the candidate locations of the reads on the reference genome are verified to compute their differences from the corresponding reference segments using sequence alignment algorithms.
GraphMineSuite: Enabling High-Performance and Programmable Graph Mining Algorithms with Set Algebra
We propose GraphMineSuite (GMS): the first benchmarking suite for graph mining that facilitates evaluating and constructing high-performance graph mining algorithms.
Mitigating Edge Machine Learning Inference Bottlenecks: An Empirical Study on Accelerating Google Edge Models
We comprehensively study the characteristics of each NN layer in all of the Google edge models, and find that these shortcomings arise from the one-size-fits-all approach of the accelerator, as there is a high amount of heterogeneity in key layer characteristics both across different models and across different layers in the same model.
COVIDHunter: An Accurate, Flexible, and Environment-Aware Open-Source COVID-19 Outbreak Simulation Model
The key idea of COVIDHunter is to quantify the spread of COVID-19 in a geographical region by simulating the average number of new infections caused by an infected person considering the effect of external factors, such as environmental conditions (e. g., climate, temperature, humidity) and mitigation measures.
Robust Machine Learning Systems: Challenges, Current Trends, Perspectives, and the Road Ahead
Machine Learning (ML) techniques have been rapidly adopted by smart Cyber-Physical Systems (CPS) and Internet-of-Things (IoT) due to their powerful decision-making capabilities.
SIMDRAM: A Framework for Bit-Serial SIMD Processing Using DRAM
Compared to a CPU and a high-end GPU, SIMDRAM is 257x and 31x more energy-efficient, while providing 93x and 6x higher operation throughput, respectively.
Hardware Architecture Distributed, Parallel, and Cluster Computing Emerging Technologies
Reducing Solid-State Drive Read Latency by Optimizing Read-Retry (Extended Abstract)
Through a detailed analysis of the read mechanism and rigorous characterization of 160 real 3D NAND flash memory chips, we find new opportunities to reduce the read-retry latency by exploiting two advanced features widely adopted in modern NAND flash-based SSDs: 1) the CACHE READ command and 2) strong ECC engine.
Hardware Architecture Distributed, Parallel, and Cluster Computing
Bit-Exact ECC Recovery (BEER): Determining DRAM On-Die ECC Functions by Exploiting DRAM Data Retention Characteristics
As a concrete example, we introduce and evaluate BEEP, the first error profiling methodology that uses the known on-die ECC function to recover the number and bit-exact locations of unobservable raw bit errors responsible for observable post-correction errors.
Hardware Architecture
GenASM: A High-Performance, Low-Power Approximate String Matching Acceleration Framework for Genome Sequence Analysis
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
An Experimental Study of Reduced-Voltage Operation in Modern FPGAs for Neural Network Acceleration
We evaluate the reliability-power trade-off for such accelerators.
Accelerating B-spline Interpolation on GPUs: Application to Medical Image Registration
In this paper, we introduce a novel GPU implementation of BSI to accelerate the calculation of the deformation field in non-rigid image registration algorithms.
Distributed, Parallel, and Cluster Computing Image and Video Processing
Technology dictates algorithms: Recent developments in read alignment
We separately discuss how longer read lengths produce unique advantages and limitations to read alignment techniques.
AirLift: A Fast and Comprehensive Technique for Remapping Alignments between Reference Genomes
There are several tools that attempt to accelerate the process of updating a read data set from one reference to another (i. e., remapping).
SneakySnake: A Fast and Accurate Universal Genome Pre-Alignment Filter for CPUs, GPUs, and FPGAs
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.
EDEN: Enabling Energy-Efficient, High-Performance Deep Neural Network Inference Using Approximate DRAM
Based on this observation, we propose EDEN, a general framework that reduces DNN energy consumption and DNN evaluation latency by using approximate DRAM devices, while strictly meeting a user-specified target DNN accuracy.
The Non-IID Data Quagmire of Decentralized Machine Learning
Our study shows that: (i) skewed data labels are a fundamental and pervasive problem for decentralized learning, causing significant accuracy loss across many ML applications, DNN models, training datasets, and decentralized learning algorithms; (ii) the problem is particularly challenging for DNN models with batch normalization; and (iii) the degree of data skew is a key determinant of the difficulty of the problem.
A Workload and Programming Ease Driven Perspective of Processing-in-Memory
First, we describe our work on systematically identifying opportunities for PIM in real applications, and quantify potential gains for popular emerging applications (e. g., machine learning, data analytics, genome analysis).
Distributed, Parallel, and Cluster Computing Hardware Architecture
Processing Data Where It Makes Sense: Enabling In-Memory Computation
This design choice goes directly against at least three key trends in systems that cause performance, scalability and energy bottlenecks: (1) data access from memory is already a key bottleneck as applications become more data-intensive and memory bandwidth and energy do not scale well, (2) energy consumption is a key constraint in especially mobile and server systems, (3) data movement is very expensive in terms of bandwidth, energy and latency, much more so than computation.
Hardware Architecture
Accelerating Generalized Linear Models with MLWeaving: A One-Size-Fits-All System for Any-precision Learning (Technical Report)
Learning from the data stored in a database is an important function increasingly available in relational engines.
Understanding the Interactions of Workloads and DRAM Types: A Comprehensive Experimental Study
As a result, the combined DRAM-workload behavior is often difficult to intuitively determine today, which can hinder memory optimizations in both hardware and software.
Hardware Architecture Performance
Apollo: A Sequencing-Technology-Independent, Scalable, and Accurate Assembly Polishing Algorithm
Our experiments with real read sets demonstrate that Apollo is the only algorithm that 1) uses reads from any sequencing technology within a single run and 2) scales well to polish large assemblies without splitting the assembly into multiple parts.
Enabling the Adoption of Processing-in-Memory: Challenges, Mechanisms, Future Research Directions
This requires efficient mechanisms that can provide logic in DRAM with access to CPU structures without having to communicate frequently with the CPU.
Hardware Architecture
Focus: Querying Large Video Datasets with Low Latency and Low Cost
Focus handles the lower accuracy of the cheap CNNs by judiciously leveraging expensive CNNs at query-time.
GRIM-Filter: Fast Seed Location Filtering in DNA Read Mapping Using Processing-in-Memory Technologies
State-of-the-art read mappers 1) quickly generate possible mapping locations for seeds (i. e., smaller segments) within each read, 2) extract reference sequences at each of the mapping locations, and 3) check similarity between each read and its associated reference sequences with a computationally-expensive algorithm (i. e., sequence alignment) to determine the origin of the read.
GateKeeper: A New Hardware Architecture for Accelerating Pre-Alignment in DNA Short Read Mapping
The addition of GateKeeper as a pre-alignment step can reduce the verification time of the mrFAST mapper by a factor of 10.
