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Found 18 papers, 5 papers with code
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
While prior methods can process up to a few thousand time steps, our model, based on modern recurrent deep state-space models, scales to event streams of millions of events for both training and inference. We leverage their stable parameterization for learning long-range dependencies, parallelizability along the sequence dimension, and their ability to integrate asynchronous events effectively to scale them up to long event streams. We further augment these with novel event-centric techniques enabling our model to match or beat the state-of-the-art performance on several event stream benchmarks.
Ranked #1 on
Audio Classification
on SSC
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
The NeuroBench framework introduces a common set of tools and systematic methodology for inclusive benchmark measurement, delivering an objective reference framework for quantifying neuromorphic approaches in both hardware-independent (algorithm track) and hardware-dependent (system track) settings.
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 #2 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.
