81 citations · 155 across the 25 of their papers we have counts for
28 papers
Efficient Vision-Language-Action Management and Serving for Robot Factories
Dionysios Adamopoulos, Nattapol Chanpaisit, Basel Fakhri +1
Vision-Language-Action (VLA) models show high robotic manipulation capabilities via a two-stage design: a Vision-Language Model (VLM) stage followed by an Action Diffusion Transfor…
ALPHA-PIM: Analysis of Linear Algebraic Processing for High-Performance Graph Applications on a Real Processing-In-Memory System
Marzieh Barkhordar, Alireza Tabatabaeian, Mohammad Sadrosadati +5
Processing large-scale graph datasets is computationally intensive and time-consuming. Processor-centric CPU and GPU architectures, commonly used for graph applications, often face…
Sparse Computations in Deep Learning Inference
Ioanna Tasou, Panagiotis Mpakos, Angelos Vlachos +25
The computational demands of modern Deep Neural Networks (DNNs) are immense and constantly growing. While training costs usually capture public attention, inference demands are als…
Spira: Exploiting Voxel Data Structural Properties for Efficient Sparse Convolution in Point Cloud Networks
Dionysios Adamopoulos, Anastasia Poulopoulou, Georgios Goumas +1
Sparse Convolution (SpC) powers 3D point cloud networks widely used in autonomous driving and augmented/virtual reality. SpC builds a kernel map that stores mappings between input…
DCC: Data-Centric Compilation of Machine Learning Kernels for Processing-In-Memory Architectures
Peiming Yang, Sankeerth Durvasula, Ivan Fernandez +4
High-performance Host processors can integrate Processing-In-Memory (PIM) devices, which can accelerate memory-intensive kernels of Machine Learning (ML) models, including Large La…
Mist: Efficient Distributed Training of Large Language Models via Memory-Parallelism Co-Optimization
Zhanda Zhu, Christina Giannoula, Muralidhar Andoorveedu +4
Various parallelism, such as data, tensor, and pipeline parallelism, along with memory optimizations like activation checkpointing, redundancy elimination, and offloading, have bee…
Seesaw: High-throughput LLM Inference via Model Re-sharding
Qidong Su, Wei Zhao, Xin Li +6
To improve the efficiency of distributed large language model (LLM) inference, various parallelization strategies, such as tensor and pipeline parallelism, have been proposed. Howe…
PAPI: Exploiting Dynamic Parallelism in Large Language Model Decoding with a Processing-In-Memory-Enabled Computing System
Yintao He, Haiyu Mao, Christina Giannoula +6
Large language models (LLMs) are widely used for natural language understanding and text generation. An LLM model relies on a time-consuming step called LLM decoding to generate ou…
Evaluating the Effectiveness of Microarchitectural Hardware Fault Detection for Application-Specific Requirements
Konstantinos-Nikolaos Papadopoulos, Christina Giannoula, Nikolaos-Charalampos Papadopoulos +3
Reliability is necessary in safety-critical applications spanning numerous domains. Conventional hardware-based fault tolerance techniques, such as component redundancy, ensure rel…
Proceedings of 3rd Workshop on Heterogeneous Composable and Disaggregated Systems
Christian Pinto, Dong Li, Thaleia Dimitra Doudali +2
The future of computing systems is inevitably embracing a disaggregated and composable pattern: from clusters of computers to pools of resources that can be dynamically combined to…
SmartPQ: An Adaptive Concurrent Priority Queue for NUMA Architectures
Christina Giannoula, Foteini Strati, Dimitrios Siakavaras +2
Concurrent priority queues are widely used in important workloads, such as graph applications and discrete event simulations. However, designing scalable concurrent priority queues…
Proteus: Preserving Model Confidentiality during Graph Optimizations
Yubo Gao, Maryam Haghifam, Christina Giannoula +3
Deep learning (DL) models have revolutionized numerous domains, yet optimizing them for computational efficiency remains a challenging endeavor. Development of new DL models typica…
PyGim: An Efficient Graph Neural Network Library for Real Processing-In-Memory Architectures
Christina Giannoula, Peiming Yang, Ivan Fernandez +7
Graph Neural Networks (GNNs) are emerging ML models to analyze graph-structure data. Graph Neural Network (GNN) execution involves both compute-intensive and memory-intensive kerne…
Minuet: Accelerating 3D Sparse Convolutions on GPUs
Jiacheng Yang, Christina Giannoula, Jun Wu +3
Sparse Convolution (SC) is widely used for processing 3D point clouds that are inherently sparse. Different from dense convolution, SC preserves the sparsity of the input point clo…
The Synergy of Speculative Decoding and Batching in Serving Large Language Models
Qidong Su, Christina Giannoula, Gennady Pekhimenko
Large Language Models (LLMs) like GPT are state-of-the-art text generation models that provide significant assistance in daily routines. However, LLM execution is inherently sequen…
Architectural Support for Efficient Data Movement in Disaggregated Systems
Christina Giannoula, Kailong Huang, Jonathan Tang +4
Resource disaggregation offers a cost effective solution to resource scaling, utilization, and failure-handling in data centers by physically separating hardware devices in a serve…
DaeMon: Architectural Support for Efficient Data Movement in Disaggregated Systems
Christina Giannoula, Kailong Huang, Jonathan Tang +4
Resource disaggregation offers a cost effective solution to resource scaling, utilization, and failure-handling in data centers by physically separating hardware devices in a serve…
Accelerating Irregular Applications via Efficient Synchronization and Data Access Techniques
Christina Giannoula
Irregular applications comprise an increasingly important workload domain for many fields, including bioinformatics, chemistry, physics, social sciences and machine learning. There…
Accelerating Time Series Analysis via Processing using Non-Volatile Memories
Ivan Fernandez, Christina Giannoula, Aditya Manglik +6
Time Series Analysis (TSA) is a critical workload to extract valuable information from collections of sequential data, e.g., detecting anomalies in electrocardiograms. Subsequence…
RevaMp3D: Architecting the Processor Core and Cache Hierarchy for Systems with Monolithically-Integrated Logic and Memory
Nika Mansouri Ghiasi, Mohammad Sadrosadati, Geraldo F. Oliveira +9
Recent nano-technological advances enable the Monolithic 3D (M3D) integration of multiple memory and logic layers in a single chip, allowing for fine-grained connections between la…
Exploiting Near-Data Processing to Accelerate Time Series Analysis
Ivan Fernandez, Ricardo Quislant, Christina Giannoula +5
Time series analysis is a key technique for extracting and predicting events in domains as diverse as epidemiology, genomics, neuroscience, environmental sciences, economics, and m…
Towards Efficient Sparse Matrix Vector Multiplication on Real Processing-In-Memory Systems
Christina Giannoula, Ivan Fernandez, Juan Gómez-Luna +3
Several manufacturers have already started to commercialize near-bank Processing-In-Memory (PIM) architectures. Near-bank PIM architectures place simple cores close to DRAM banks a…
SparseP: Towards Efficient Sparse Matrix Vector Multiplication on Real Processing-In-Memory Systems
Christina Giannoula, Ivan Fernandez, Juan Gómez-Luna +3
Several manufacturers have already started to commercialize near-bank Processing-In-Memory (PIM) architectures. Near-bank PIM architectures place simple cores close to DRAM banks a…
Benchmarking Memory-Centric Computing Systems: Analysis of Real Processing-in-Memory Hardware
Juan Gómez-Luna, Izzat El Hajj, Ivan Fernandez +3
Many modern workloads such as neural network inference and graph processing are fundamentally memory-bound. For such workloads, data movement between memory and CPU cores imposes a…