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20 results for “compute”

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cs.CRRecentMay 13, 2026

HE-PIM: Demystifying Homomorphic Operations on a Real-world Processing-in-Memory System

Harshita Gupta, Mayank Kabra, Jaewoo Park, Priyam Mehta +8 more

The paper characterizes Homomorphic Encryption (HE) operations on a real-world Processing-In-Memory (PIM) system, demonstrating that while PIM is a viable alternative to CPUs/GPUs, performance is limi…

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cs.CRcs.ARcs.LGRecentMar 20, 2026

Hawkeye: Reproducing GPU-Level Non-Determinism

Erez Badash, Dan Boneh, Ilan Komargodski, Megha Srivastava

Hawkeye is a system that allows perfect, precision-preserving reproduction of GPU-level matrix multiplication operations on a CPU, enabling efficient and trustworthy third-party auditing of machine le…

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cs.DScs.DCcs.MSEmpiricalRecentJul 27, 2026

Right Multiplication on Grammar-Compressed Matrices: A Streaming, Memory-Bounded GPU Engine

Francesco Tosoni, Gabriele Mencagli

This paper presents a method for compressing matrices using a RePair straight-line program (SLP), allowing matrix-vector products with time and space proportional to the compressed size, and demonstra…

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cs.PLcs.CCcs.FLRecentMay 30, 2026

Grid Programs: A Two-Dimensional, Variable-Free Model of Computation

Ezequiel López-Rubio

The paper introduces Grid Programs, a novel, Turing-complete model of computation where programs are two-dimensional arrangements of instructions, fundamentally departing from linear code structures.

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cs.ARcs.LGEmpiricalRecentJun 28, 2026

Harvesting AI Computation at the Edge via Generic Approximation

Yihan Wang, Huiru Yan, Luxin Zhang, Long Cheng +5 more

The paper proposes a framework to harvest unused computation resources on AI chips for general-purpose tasks using neural architecture search and approximation techniques.

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cs.AREmpiricalRecentJul 24, 2026

The Sparsity Tax: Weight Sparsity Trade-offs in Event-Driven SIMD and SIMT Neuromorphic Cores

Mattias Westerink, Sameed Sohail, Berend-Jan van der Zwaag, Sabih Gerez +1 more

This paper compares three neuromorphic core designs for handling weight sparsity in event-driven neural networks and quantifies the performance and energy costs.

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cs.ARRecentJun 1, 2026

O-POPE: High-Frequency Pipelined Outer Product based GEMM acceleration with minimal buffering overhead

Danilo Cammarata, Angelo Garofalo, Luca Benini

O-POPE is a novel outer-product engine that accelerates floating-point GEMM by repurposing FPU pipeline registers as buffers, achieving high utilization and improved energy efficiency.

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cs.DCcs.AIEmpiricalRecentJul 21, 2026

Fine-grained Computation-Communication Overlap via Tile-level Signaling and Scheduling for Mixture-of-Experts

Minyu Cui, Anna Wingkvist, Morgan Ericsson

The paper proposes a fine-grained approach to overlap expert compute with the second all-to-all communication in Mixture-of-Experts (MoE) models, improving distributed MoE execution efficiency on mult…

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cs.ARcs.LGcs.OSEmpiricalRecentJul 16, 2026

ExaGEMM: Exploration Framework for CPU-Driven ML Inference via Associative In-Register Computing for Low-Bit GEMM

Hyunwoo Oh, Suyeon Jang, Hanning Chen, Sanggeon Yun +2 more

The paper presents ExaGEMM, a framework for designing and exploring CPU-native low-bit GEMM via register-resident LUT execution.

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cs.ARcs.AIEmpiricalRecentJul 24, 2026

Sparse by Command: Task-Conditional Compute Skipping for Multi-Task Inference Accelerators

Afzal Ahmad, Gaoyu Mao, Shoubo Hu, Hui-Ling Zhen +3 more

A co-designed hardware-software approach for task-conditional sparsity in multi-task inference models, reducing FLOPs, latency, and energy.

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cs.CRRecentApr 21, 2026

Efficient Arithmetic-and-Comparison Homomorphic Encryption with Space Switching

Erwin Eko Wahyudi, Yan Solihin, Qian Lou

The paper proposes a novel space switching method to efficiently unify arithmetic and comparison operations within Fully Homomorphic Encryption (FHE) schemes, achieving significant performance improve…

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cs.AREmpiricalRecentJul 21, 2026

An Efficient Fault-Tolerance Scheme for CKKS Computation on CPUs

Jianan Mu, Ge Yu, Tenghui Hua, Liang Kong +5 more

This paper presents an efficient fault-tolerance scheme for CPU-based fully homomorphic encryption (FHE) that reduces protection overhead and achieves 100 percent detection rate under random single-bi…

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cs.DCEmpiricalRecentJun 19, 2026

rush: Scalable Asynchronous Distributed Computing via Shared State in R

Marc Becker, Bernd Bischl

An R package named rush is introduced, which provides a shared-state coordination layer for asynchronously parallelized iterative algorithms using a Redis database.

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cs.DCq-bio.NCEmpiricalRecentJul 24, 2026

NUMA balancing hampering performance of spiking network simulations

Melissa Lober, Alp Inangu, Gorka Peraza Coppola, Dennis Terhorst +8 more

Turning off automatic NUMA balancing in simulation of large-scale spiking networks reduces energy consumption by 30%.

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