Copertina

Column-wise Quantization of Weights and Partial Sums for Accurate and Efficient Compute-In-Memory Accelerators

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Kim, Jiyoon, Jeon, Kang Eun, Kim, Yulhwa, Ko, Jong Hwan
Natura:	Preprint
Pubblicazione:	2025
Soggetti:	Hardware Architecture Artificial Intelligence Machine Learning
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

MEMHD: Memory-Efficient Multi-Centroid Hyperdimensional Computing for Fully-Utilized In-Memory Computing Architectures
di: Kang, Do Yeong, et al.
Pubblicazione: (2025)

Row-Column Hybrid Grouping for Fault-Resilient Multi-Bit Weight Representation on IMC Arrays
di: Jeon, Kang Eun, et al.
Pubblicazione: (2025)

Low-Rank Compression for IMC Arrays
di: Jeon, Kang Eun, et al.
Pubblicazione: (2025)

StoX-Net: Stochastic Processing of Partial Sums for Efficient In-Memory Computing DNN Accelerators
di: Rogers, Ethan G, et al.
Pubblicazione: (2024)

APSQ: Additive Partial Sum Quantization with Algorithm-Hardware Co-Design
di: Tan, Yonghao, et al.
Pubblicazione: (2025)

ProactivePIM: Accelerating Weight-Sharing Embedding Layer with PIM for Scalable Recommendation System
di: Kim, Youngsuk, et al.
Pubblicazione: (2024)

FastMamba: A High-Speed and Efficient Mamba Accelerator on FPGA with Accurate Quantization
di: Wang, Aotao, et al.
Pubblicazione: (2025)

Accelerating LLM Inference with Flexible N:M Sparsity via A Fully Digital Compute-in-Memory Accelerator
di: Ramachandran, Akshat, et al.
Pubblicazione: (2025)

NeFT: Negative Feedback Training to Improve Robustness of Compute-In-Memory DNN Accelerators
di: Qin, Yifan, et al.
Pubblicazione: (2023)

Binary Weight Multi-Bit Activation Quantization for Compute-in-Memory CNN Accelerators
di: Zhou, Wenyong, et al.
Pubblicazione: (2025)

A 28 nm AI microcontroller with tightly coupled zero-standby power weight memory featuring standard logic compatible 4 Mb 4-bits/cell embedded flash technology
di: Kim, Daewung, et al.
Pubblicazione: (2025)

Efficient Calibration for RRAM-based In-Memory Computing using DoRA
di: Dong, Weirong, et al.
Pubblicazione: (2025)

HiKonv: Maximizing the Throughput of Quantized Convolution With Novel Bit-wise Management and Computation
di: Chen, Yao, et al.
Pubblicazione: (2022)

Sorted Weight Sectioning for Energy-Efficient Unstructured Sparse DNNs on Compute-in-Memory Crossbars
di: Farias, Matheus, et al.
Pubblicazione: (2024)

RangeGuard: Efficient, Bounded Approximate Error Correction for Reliable DNNs
di: Ko, Hanum, et al.
Pubblicazione: (2026)

FASQ: Flexible Accelerated Subspace Quantization for Calibration-Free LLM Compression
di: Qiao, Ye, et al.
Pubblicazione: (2026)

AQPIM: Breaking the PIM Capacity Wall for LLMs with In-Memory Activation Quantization
di: Matsushima, Kosuke, et al.
Pubblicazione: (2026)

ElasticAI: Creating and Deploying Energy-Efficient Deep Learning Accelerator for Pervasive Computing
di: Qian, Chao, et al.
Pubblicazione: (2024)

Fast, Scalable, Energy-Efficient Non-element-wise Matrix Multiplication on FPGA
di: Zhu, Xuqi, et al.
Pubblicazione: (2024)

Graph Computation Meets Circuit Algebra: A Task-Aligned Analysis of Graph Neural Networks for Electronic Design Automation
di: Kim, Hyunmog
Pubblicazione: (2026)

SmartQuant: CXL-based AI Model Store in Support of Runtime Configurable Weight Quantization
di: Xie, Rui, et al.
Pubblicazione: (2024)

MicroScopiQ: Accelerating Foundational Models through Outlier-Aware Microscaling Quantization
di: Ramachandran, Akshat, et al.
Pubblicazione: (2024)

NeuroSim V1.5: Improved Software Backbone for Benchmarking Compute-in-Memory Accelerators with Device and Circuit-level Non-idealities
di: Read, James, et al.
Pubblicazione: (2025)

A Configurable and Efficient Memory Hierarchy for Neural Network Hardware Accelerator
di: Bause, Oliver, et al.
Pubblicazione: (2024)

vTrain: A Simulation Framework for Evaluating Cost-effective and Compute-optimal Large Language Model Training
di: Bang, Jehyeon, et al.
Pubblicazione: (2023)

Layer-wise Weight Selection for Power-Efficient Neural Network Acceleration
di: Fang, Jiaxun, et al.
Pubblicazione: (2025)

MICSim: A Modular Simulator for Mixed-signal Compute-in-Memory based AI Accelerator
di: Wang, Cong, et al.
Pubblicazione: (2024)

MixPE: Quantization and Hardware Co-design for Efficient LLM Inference
di: Zhang, Yu, et al.
Pubblicazione: (2024)

Efficient Deployment of CNN Models on Multiple In-Memory Computing Units
di: Bougioukou, Eleni, et al.
Pubblicazione: (2025)

Leveraging Compute-in-Memory for Efficient Generative Model Inference in TPUs
di: Zhu, Zhantong, et al.
Pubblicazione: (2025)

The Role of Advanced Computer Architectures in Accelerating Artificial Intelligence Workloads
di: Amin, Shahid, et al.
Pubblicazione: (2025)

Unveiling the Potential of Quantization with MXFP4: Strategies for Quantization Error Reduction
di: Chhugani, Jatin, et al.
Pubblicazione: (2026)

TReX- Reusing Vision Transformer's Attention for Efficient Xbar-based Computing
di: Moitra, Abhishek, et al.
Pubblicazione: (2024)

TriGen: NPU Architecture for End-to-End Acceleration of Large Language Models based on SW-HW Co-Design
di: Lee, Jonghun, et al.
Pubblicazione: (2026)

SPADE: Sparse Pillar-based 3D Object Detection Accelerator for Autonomous Driving
di: Lee, Minjae, et al.
Pubblicazione: (2023)

Hardware-Software Co-Design for Accelerating Transformer Inference Leveraging Compute-in-Memory
di: Kim, Dong Eun, et al.
Pubblicazione: (2025)

Automatic Generation of Fast and Accurate Performance Models for Deep Neural Network Accelerators
di: Lübeck, Konstantin, et al.
Pubblicazione: (2024)

APT-LLM: Exploiting Arbitrary-Precision Tensor Core Computing for LLM Acceleration
di: Ma, Shaobo, et al.
Pubblicazione: (2025)

ApproXAI: Energy-Efficient Hardware Acceleration of Explainable AI using Approximate Computing
di: Siddique, Ayesha, et al.
Pubblicazione: (2025)

LUT-LLM: Efficient Large Language Model Inference with Memory-based Computations on FPGAs
di: He, Zifan, et al.
Pubblicazione: (2025)