Cover Image

Bayesian Inference on Binary Spiking Networks Leveraging Nanoscale Device Stochasticity

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Katti, Prabodh, Skatchkovsky, Nicolas, Simeone, Osvaldo, Rajendran, Bipin, Al-Hashimi, Bashir M.
Format:	Preprint
Published:	2023
Subjects:	Neural and Evolutionary Computing Hardware Architecture Emerging Technologies Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Bayes2IMC: In-Memory Computing for Bayesian Binary Neural Networks
by: Katti, Prabodh, et al.
Published: (2024)

Stochastic Spiking Attention: Accelerating Attention with Stochastic Computing in Spiking Networks
by: Song, Zihang, et al.
Published: (2024)

Sparsity-Aware Optimization of In-Memory Bayesian Binary Neural Network Accelerators
by: Katti, Prabodh, et al.
Published: (2024)

First Demonstration of Second-order Training of Deep Neural Networks with In-memory Analog Matrix Computing
by: Zhang, Saitao, et al.
Published: (2025)

Event-based backpropagation on the neuromorphic platform SpiNNaker2
by: Béna, Gabriel, et al.
Published: (2024)

Trilinear Compute-in-Memory Architecture for Energy-Efficient Transformer Acceleration
by: Mia, Md Zesun Ahmed, et al.
Published: (2026)

VVTEAM: A Compact Behavioral Model for Volatile Memristors
by: Patni, Tanay, et al.
Published: (2024)

Preprocessing Methods for Memristive Reservoir Computing for Image Recognition
by: Daniels, Rishona, et al.
Published: (2025)

Bayesian Inference Accelerator for Spiking Neural Networks
by: Katti, Prabodh, et al.
Published: (2024)

Xpikeformer: Hybrid Analog-Digital Hardware Acceleration for Spiking Transformers
by: Song, Zihang, et al.
Published: (2024)

Mean-Field Assisted Deep Boltzmann Learning with Probabilistic Computers
by: Chowdhury, Shuvro, et al.
Published: (2024)

Analog In-Memory Computing Attention Mechanism for Fast and Energy-Efficient Large Language Models
by: Leroux, Nathan, et al.
Published: (2024)

TEXEL: A neuromorphic processor with on-chip learning for beyond-CMOS device integration
by: Greatorex, Hugh, et al.
Published: (2024)

CIMNAS: A Joint Framework for Compute-In-Memory-Aware Neural Architecture Search
by: Krestinskaya, Olga, et al.
Published: (2025)

A Comparative Analysis of Microrings Based Incoherent Photonic GEMM Accelerators
by: Vatsavai, Sairam Sri, et al.
Published: (2024)

Dynamic neural network with memristive CIM and CAM for 2D and 3D vision
by: Zhang, Yue, et al.
Published: (2024)

Approximate ADCs for In-Memory Computing
by: Ghosh, Arkapravo, et al.
Published: (2024)

Resistive Memory-based Neural Differential Equation Solver for Score-based Diffusion Model
by: Yang, Jichang, et al.
Published: (2024)

Continuous-Time Digital Twin with Analogue Memristive Neural Ordinary Differential Equation Solver
by: Chen, Hegan, et al.
Published: (2024)

Hardware-Software Co-optimised Fast and Accurate Deep Reconfigurable Spiking Inference Accelerator Architecture Design Methodology
by: Nimbekar, Anagha, et al.
Published: (2024)

Joint Hardware-Workload Co-Optimization for In-Memory Computing Accelerators
by: Krestinskaya, Olga, et al.
Published: (2026)

HOMI: Ultra-Fast EdgeAI platform for Event Cameras
by: H, Shankaranarayanan, et al.
Published: (2025)

EvGNN: An Event-driven Graph Neural Network Accelerator for Edge Vision
by: Yang, Yufeng, et al.
Published: (2024)

Integrated Security Mechanisms for Weight Protection in Memristive Crossbar Arrays
by: Rahman, Muhammad Faheemur, et al.
Published: (2025)

DendroNN: Dendrocentric Neural Networks for Energy-Efficient Classification of Event-Based Data
by: Krausse, Jann, et al.
Published: (2026)

On the Role of Preprocessing and Memristor Dynamics in Reservoir Computing for Image Classification
by: Daniels, Rishona, et al.
Published: (2026)

Reservoir Computing with Magnetic Thin Films
by: Dale, Matthew, et al.
Published: (2021)

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

Spiking Transformer Hardware Accelerators in 3D Integration
by: Xu, Boxun, et al.
Published: (2024)

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

Analog-to-Stochastic Converter Using Magnetic Tunnel Junction Devices for Vision Chips
by: Onizawa, Naoya, et al.
Published: (2026)

Hardware-Friendly Implementation of Physical Reservoir Computing with CMOS-based Time-domain Analog Spiking Neurons
by: Kimura, Nanako, et al.
Published: (2024)

E2ATST: A Temporal-Spatial Optimized Energy-Efficient Architecture for Training Spiking Transformer
by: Ma, Yunhao, et al.
Published: (2025)

A 0.5-V Linear Neuromorphic Voltage-to-Spike Encoder Using a Bulk-Driven Transconductor
by: Akbari, Meysam, et al.
Published: (2026)

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

Towards Efficient and Reliable AI Through Neuromorphic Principles
by: Rajendran, Bipin, et al.
Published: (2023)

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

To Spike or Not to Spike, that is the Question
by: Takaghaj, Sanaz Mahmoodi, et al.
Published: (2024)

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

Stochastic Quantum Spiking Neural Networks with Quantum Memory and Local Learning
by: Chen, Jiechen, et al.
Published: (2025)