Copertina

Attention, Distillation, and Tabularization: Towards Practical Neural Network-Based Prefetching

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Zhang, Pengmiao, Gupta, Neelesh, Kannan, Rajgopal, Prasanna, Viktor K.
Natura:	Preprint
Pubblicazione:	2023
Soggetti:	Neural and Evolutionary Computing Hardware Architecture Machine Learning Operating Systems
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

PaCKD: Pattern-Clustered Knowledge Distillation for Compressing Memory Access Prediction Models
di: Gupta, Neelesh, et al.
Pubblicazione: (2024)

Brain-inspired AI for Edge Intelligence: a systematic review
di: Cheng, Yingchao, et al.
Pubblicazione: (2026)

A Persistent-State Dataflow Accelerator for Memory-Bound Linear Attention Decode on FPGA
di: Gupta, Neelesh, et al.
Pubblicazione: (2026)

A Deep Neural Network Deployment Based on Resistive Memory Accelerator Simulation
di: Maram, Tejaswanth Reddy, et al.
Pubblicazione: (2023)

A Reconfigurable Stream-Based FPGA Accelerator for Bayesian Confidence Propagation Neural Networks
di: Hafiz, Muhammad Ihsan Al, et al.
Pubblicazione: (2025)

Energy-Aware FPGA Implementation of Spiking Neural Network with LIF Neurons
di: Ali, Asmer Hamid, et al.
Pubblicazione: (2024)

Resource-Efficient and Robust Inference of Deep and Bayesian Neural Networks on Embedded and Analog Computing Platforms
di: Klein, Bernhard
Pubblicazione: (2025)

HENNC: Hardware Engine for Artificial Neural Network-based Chaotic Oscillators
di: Vaziri, Mobin, et al.
Pubblicazione: (2024)

SpiDR: A Reconfigurable Digital Compute-in-Memory Spiking Neural Network Accelerator for Event-based Perception
di: Sharma, Deepika, et al.
Pubblicazione: (2024)

Functional Stability of Software-Hardware Neural Network Implementation The NeuroComp Project
di: Oleksii, Bychkov, et al.
Pubblicazione: (2025)

Hardware-Aware Model Design and Training of Silicon-based Analog Neural Networks
di: Filippeschi, Giulio, et al.
Pubblicazione: (2025)

Wet TinyML: Chemical Neural Network Using Gene Regulation and Cell Plasticity
di: Somathilaka, Samitha, et al.
Pubblicazione: (2024)

Catwalk: Unary Top-K for Efficient Ramp-No-Leak Neuron Design for Temporal Neural Networks
di: Lister, Devon, et al.
Pubblicazione: (2025)

UniSpike: Accelerating Spiking Neural Networks on Neuromorphic Systems via Eliminating Address Redundancy
di: Xing, Qinghui, et al.
Pubblicazione: (2026)

Core Placement Optimization of Many-core Brain-Inspired Near-Storage Systems for Spiking Neural Network Training
di: Zhu, Xueke, et al.
Pubblicazione: (2024)

SNAP-V: A RISC-V SoC with Configurable Neuromorphic Acceleration for Small-Scale Spiking Neural Networks
di: Gunawardana, Kanishka, et al.
Pubblicazione: (2026)

TabConv: Low-Computation CNN Inference via Table Lookups
di: Gupta, Neelesh, et al.
Pubblicazione: (2024)

Hybrid Synaptic Structure for Spiking Neural Network Realization
di: Razmkhah, Sasan, et al.
Pubblicazione: (2023)

Fast Algorithms for Spiking Neural Network Simulation with FPGAs
di: Lindqvist, Björn A., et al.
Pubblicazione: (2024)

ReLANCE: A Resource-Efficient Low-Latency Cortical Neural Acceleration Engine
di: Kumar, Sonu, et al.
Pubblicazione: (2025)

Analysis of a Memcapacitor-Based for Neural Network Accelerator Framework
di: Singh, Ankur, et al.
Pubblicazione: (2025)

IzhiRISC-V -- a RISC-V-based Processor with Custom ISA Extension for Spiking Neuron Networks Processing with Izhikevich Neurons
di: Szczerek, Wiktor J., et al.
Pubblicazione: (2025)

SRAM-Based Compute-in-Memory Accelerator for Linear-decay Spiking Neural Networks
di: Shang, Hongyang, et al.
Pubblicazione: (2026)

FlexNN: A Dataflow-aware Flexible Deep Learning Accelerator for Energy-Efficient Edge Devices
di: Raha, Arnab, et al.
Pubblicazione: (2024)

Towards 3D Acceleration for low-power Mixture-of-Experts and Multi-Head Attention Spiking Transformers
di: Xu, Boxun, et al.
Pubblicazione: (2024)

Demonstrating Analog Inference on the BrainScaleS-2 Mobile System
di: Stradmann, Yannik, et al.
Pubblicazione: (2021)

When Spike Sparsity Does Not Translate to Deployed Cost: VS-WNO on Jetson Orin Nano
di: Yoo, Jason, et al.
Pubblicazione: (2026)

Optimal Gradient Checkpointing for Sparse and Recurrent Architectures using Off-Chip Memory
di: Bencheikh, Wadjih, et al.
Pubblicazione: (2024)

A Realistic Simulation Framework for Analog/Digital Neuromorphic Architectures
di: Quintana, Fernando M., et al.
Pubblicazione: (2024)

Advancing Neuromorphic Computing: Mixed-Signal Design Techniques Leveraging Brain Code Units and Fundamental Code Units
di: Isik, Murat, et al.
Pubblicazione: (2024)

Stuck-at Faults in ReRAM Neuromorphic Circuit Array and their Correction through Machine Learning
di: Sawal, Vedant, et al.
Pubblicazione: (2024)

Genetic Quantization-Aware Approximation for Non-Linear Operations in Transformers
di: Dong, Pingcheng, et al.
Pubblicazione: (2024)

Embedding Hardware Approximations in Discrete Genetic-based Training for Printed MLPs
di: Afentaki, Florentia, et al.
Pubblicazione: (2024)

Towards Topology-Aware Very Large-Scale Photonic AI Accelerators
di: Jahannia, Belal, et al.
Pubblicazione: (2026)

Enabling Efficient Processing of Spiking Neural Networks with On-Chip Learning on Commodity Neuromorphic Processors for Edge AI Systems
di: Putra, Rachmad Vidya Wicaksana, et al.
Pubblicazione: (2025)

SpikeX: Exploring Accelerator Architecture and Network-Hardware Co-Optimization for Sparse Spiking Neural Networks
di: Xu, Boxun, et al.
Pubblicazione: (2025)

When In-memory Computing Meets Spiking Neural Networks -- A Perspective on Device-Circuit-System-and-Algorithm Co-design
di: Moitra, Abhishek, et al.
Pubblicazione: (2024)

Darwin3: A large-scale neuromorphic chip with a Novel ISA and On-Chip Learning
di: Ma, De, et al.
Pubblicazione: (2023)

Variability-Aware Approximate Circuit Synthesis via Genetic Optimization
di: Balaskas, Konstantinos, et al.
Pubblicazione: (2023)

NeuroFlex: Column-Exact ANN-SNN Co-Execution Accelerator with Cost-Guided Scheduling
di: Manjunath, Varun, et al.
Pubblicazione: (2025)