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Found 23 papers, 6 papers with code
Event-based backpropagation on the neuromorphic platform SpiNNaker2
Efficient training of neural networks on neuromorphic hardware requires the development of training algorithms that retain the sparsity of spike-based communication during training.
STREAM: A Universal State-Space Model for Sparse Geometric Data
Handling sparse and unstructured geometric data, such as point clouds or event-based vision, is a pressing challenge in the field of machine vision.
68-Channel Highly-Integrated Neural Signal Processing PSoC with On-Chip Feature Extraction, Compression, and Hardware Accelerators for Neuroprosthetics in 22nm FDSOI
Multi-channel electrophysiology systems for recording of neuronal activity face significant data throughput limitations, hampering real-time, data-informed experiments.
ON-OFF Neuromorphic ISING Machines using Fowler-Nordheim Annealers
We introduce NeuroSA, a neuromorphic architecture specifically designed to ensure asymptotic convergence to the ground state of an Ising problem using an annealing process that is governed by the physics of quantum mechanical tunneling using Fowler-Nordheim (FN).
Weight Sparsity Complements Activity Sparsity in Neuromorphic Language Models
Activity and parameter sparsity are two standard methods of making neural networks computationally more efficient.
Scalable Event-by-event Processing of Neuromorphic Sensory Signals With Deep State-Space Models
Event-based sensors are well suited for real-time processing due to their fast response times and encoding of the sensory data as successive temporal differences.
Ranked #1 on
Audio Classification
on SSC
Neuromorphic hardware for sustainable AI data centers
In this article, we try to analyze the underlying reasons for this and derive requirements and guidelines to promote neuromorphic systems for efficient and sustainable cloud computing: We first review currently available neuromorphic hardware systems and collect examples where neuromorphic solutions excel conventional AI processing on CPUs and GPUs.
SpiNNaker2: A Large-Scale Neuromorphic System for Event-Based and Asynchronous Machine Learning
This development is accompanied by a rapid growth of the required computational demands for larger models and more data.
Language Modeling on a SpiNNaker 2 Neuromorphic Chip
In this work, we demonstrate the first-ever implementation of a language model on a neuromorphic device - specifically the SpiNNaker 2 chip - based on a recently published event-based architecture called the EGRU.
Activity Sparsity Complements Weight Sparsity for Efficient RNN Inference
Yet, sparse activations, while omnipresent in both biological neural networks and deep learning systems, have not been fully utilized as a compression technique in deep learning.
Block-local learning with probabilistic latent representations
In addition, back-propagation relies on the transpose of forward weight matrices to compute updates, introducing a weight transport problem across the network.
NeuroBench: A Framework for Benchmarking Neuromorphic Computing Algorithms and Systems
To address these shortcomings, we present NeuroBench: a benchmark framework for neuromorphic computing algorithms and systems.
Deploying Machine Learning Models to Ahead-of-Time Runtime on Edge Using MicroTVM
In the past few years, more and more AI applications have been applied to edge devices.
Efficient recurrent architectures through activity sparsity and sparse back-propagation through time
However, there is still a need to bridge the gap between what RNNs are capable of in terms of efficiency and performance and real-world application requirements.
Ranked #3 on
Gesture Recognition
on DVS128 Gesture
(using extra training data)
Robust Environment Perception for Automated Driving: A Unified Learning Pipeline for Visual-Infrared Object Detection
The RGB complementary metal-oxidesemiconductor (CMOS) sensor works within the visible light spectrum.
Time-coded Spiking Fourier Transform in Neuromorphic Hardware
After several decades of continuously optimizing computing systems, the Moore's law is reaching itsend.
Low-Power Low-Latency Keyword Spotting and Adaptive Control with a SpiNNaker 2 Prototype and Comparison with Loihi
We implemented two neural network based benchmark tasks on a prototype chip of the second-generation SpiNNaker (SpiNNaker 2) neuromorphic system: keyword spotting and adaptive robotic control.
The Operating System of the Neuromorphic BrainScaleS-1 System
We present operation and development methodologies implemented for the BrainScaleS-1 neuromorphic architecture and walk through the individual components of BrainScaleS OS constituting the software stack for BrainScaleS-1 platform operation.
Dynamic Power Management for Neuromorphic Many-Core Systems
By measurement of three neuromorphic benchmarks it is shown that the total PE power consumption can be reduced by 75%, with 80% baseline power reduction and a 50% reduction of energy per neuron and synapse computation, all while maintaining temporary peak system performance to achieve biological real-time operation of the system.
Efficient Reward-Based Structural Plasticity on a SpiNNaker 2 Prototype
Advances in neuroscience uncover the mechanisms employed by the brain to efficiently solve complex learning tasks with very limited resources.
Pattern representation and recognition with accelerated analog neuromorphic systems
Despite being originally inspired by the central nervous system, artificial neural networks have diverged from their biological archetypes as they have been remodeled to fit particular tasks.
Neuromorphic Hardware In The Loop: Training a Deep Spiking Network on the BrainScaleS Wafer-Scale System
In this paper, we demonstrate how iterative training of a hardware-emulated network can compensate for anomalies induced by the analog substrate.
Waveform Driven Plasticity in BiFeO3 Memristive Devices: Model and Implementation
To the best of our knowledge, this is the first implementations of triplet plasticity on any physical memristive device.
