Cover Image

iEEG Seizure Detection with a Sparse Hyperdimensional Computing Accelerator

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Cuyckens, Stef, Antonio, Ryan, Fang, Chao, Verhelst, Marian
Format:	Preprint
Published:	2025
Subjects:	Hardware Architecture Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Precision-Scalable Microscaling Datapaths with Optimized Reduction Tree for Efficient NPU Integration
by: Cuyckens, Stef, et al.
Published: (2025)

Efficient Precision-Scalable Hardware for Microscaling (MX) Processing in Robotics Learning
by: Cuyckens, Stef, et al.
Published: (2025)

P3-LLM: An Integrated NPU-PIM Accelerator for Edge LLM Inference Using Hybrid Numerical Formats
by: Chen, Yuzong, et al.
Published: (2025)

Anda: Unlocking Efficient LLM Inference with a Variable-Length Grouped Activation Data Format
by: Fang, Chao, et al.
Published: (2024)

FSL-HDnn: A 5.7 TOPS/W End-to-end Few-shot Learning Classifier Accelerator with Feature Extraction and Hyperdimensional Computing
by: Yang, Haichao, et al.
Published: (2024)

MC$^2$A: Enabling Algorithm-Hardware Co-Design for Efficient Markov Chain Monte Carlo Acceleration
by: Zhao, Shirui, et al.
Published: (2025)

Towards Efficient Hyperdimensional Computing Using Photonics
by: Fayza, Farbin, et al.
Published: (2023)

Clo-HDnn: A 4.66 TFLOPS/W and 3.78 TOPS/W Continual On-Device Learning Accelerator with Energy-efficient Hyperdimensional Computing via Progressive Search
by: Song, Chang Eun, et al.
Published: (2025)

Fine-Grained Fusion: The Missing Piece in Area-Efficient State Space Model Acceleration
by: Geens, Robin, et al.
Published: (2025)

ESACT: An End-to-End Sparse Accelerator for Compute-Intensive Transformers via Local Similarity
by: Liu, Hongxiang, et al.
Published: (2025)

DataMaestro: A Versatile and Efficient Data Streaming Engine Bringing Decoupled Memory Access To Dataflow Accelerators
by: Yi, Xiaoling, et al.
Published: (2025)

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

Hardware-Centric Analysis of DeepSeek's Multi-Head Latent Attention
by: Geens, Robin, et al.
Published: (2025)

Efficient In-Memory Acceleration of Sparse Block Diagonal LLMs
by: de Lima, João Paulo Cardoso, et al.
Published: (2025)

Enabling Efficient Hardware Acceleration of Hybrid Vision Transformer (ViT) Networks at the Edge
by: Dumoulin, Joren, et al.
Published: (2025)

Hardware Generation and Exploration of Lookup Table-Based Accelerators for 1.58-bit LLM Inference
by: Geens, Robin, et al.
Published: (2026)

Accelerating Sparse Graph Neural Networks with Tensor Core Optimization
by: Wu, Ka Wai
Published: (2024)

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

FLAASH: Flexible Accelerator Architecture for Sparse High-Order Tensor Contraction
by: Kulp, Gabriel, et al.
Published: (2024)

Enabling Unstructured Sparse Acceleration on Structured Sparse Accelerators
by: Jeong, Geonhwa, et al.
Published: (2024)

A 16 nm 1.60TOPS/W High Utilization DNN Accelerator with 3D Spatial Data Reuse and Efficient Shared Memory Access
by: Yi, Xiaoling, et al.
Published: (2026)

Efficient Implementation of LinearUCB through Algorithmic Improvements and Vector Computing Acceleration for Embedded Learning Systems
by: Angioli, Marco, et al.
Published: (2025)

Accelerating Computer Architecture Simulation through Machine Learning
by: Ali, Wajid, et al.
Published: (2024)

Sustainable Transformer Neural Network Acceleration with Stochastic Photonic Computing
by: Afifi, S., et al.
Published: (2026)

FedUHD: Unsupervised Federated Learning using Hyperdimensional Computing
by: Lee, You Hak, et al.
Published: (2025)

HDReason: Algorithm-Hardware Codesign for Hyperdimensional Knowledge Graph Reasoning
by: Chen, Hanning, et al.
Published: (2024)

How to keep pushing ML accelerator performance? Know your rooflines!
by: Verhelst, Marian, et al.
Published: (2025)

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

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

U-SWIM: Universal Selective Write-Verify for Computing-in-Memory Neural Accelerators
by: Yan, Zheyu, et al.
Published: (2023)

An Analog and Digital Hybrid Attention Accelerator for Transformers with Charge-based In-memory Computing
by: Moradifirouzabadi, Ashkan, et al.
Published: (2024)

Optical Computing for Deep Neural Network Acceleration: Foundations, Recent Developments, and Emerging Directions
by: Pasricha, Sudeep
Published: (2024)

Stream: Design Space Exploration of Layer-Fused DNNs on Heterogeneous Dataflow Accelerators
by: Symons, Arne, et al.
Published: (2022)

VEXP: A Low-Cost RISC-V ISA Extension for Accelerated Softmax Computation in Transformers
by: Wang, Run, et al.
Published: (2025)

When Small Variations Become Big Failures: Reliability Challenges in Compute-in-Memory Neural Accelerators
by: Qin, Yifan, et al.
Published: (2026)

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

A Computing-in-Memory-based One-Class Hyperdimensional Computing Model for Outlier Detection
by: Wang, Ruixuan, et al.
Published: (2023)

SafeCiM: Investigating Resilience of Hybrid Floating-Point Compute-in-Memory Deep Learning Accelerators
by: Bhattacharya, Swastik, et al.
Published: (2025)

Memory Is All You Need: An Overview of Compute-in-Memory Architectures for Accelerating Large Language Model Inference
by: Wolters, Christopher, et al.
Published: (2024)

XDMA: A Distributed, Extensible DMA Architecture for Layout-Flexible Data Movements in Heterogeneous Multi-Accelerator SoCs
by: Kong, Fanchen, et al.
Published: (2025)