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Found 21 papers, 2 papers with code
LMUFormer: Low Complexity Yet Powerful Spiking Model With Legendre Memory Units
Moreover, we present a spiking version of this architecture, which introduces the benefit of states within the patch embedding and channel mixer modules while simultaneously reducing the computing complexity.
When Bio-Inspired Computing meets Deep Learning: Low-Latency, Accurate, & Energy-Efficient Spiking Neural Networks from Artificial Neural Networks
However, advanced ANN-to-SNN conversion approaches demonstrate that for lossless conversion, the number of SNN time steps must equal the number of quantization steps in the ANN activation function.
Spiking Neural Networks with Dynamic Time Steps for Vision Transformers
Recently proposed SNN training algorithms have significantly reduced the number of time steps (down to 1) for improved latency and energy efficiency, however, they target only convolutional neural networks (CNN).
FixPix: Fixing Bad Pixels using Deep Learning
Efficient and effective on-line detection and correction of bad pixels can improve yield and increase the expected lifetime of image sensors.
Let's Roll: Synthetic Dataset Analysis for Pedestrian Detection Across Different Shutter Types
This implies that ML pipelines might not need explicit correction for RS for many object detection applications, but mitigating RS effects in ISP-less ML pipelines that target fine-grained location of the objects may need additional research.
FireFly A Synthetic Dataset for Ember Detection in Wildfire
This paper presents "FireFly", a synthetic dataset for ember detection created using Unreal Engine 4 (UE4), designed to overcome the current lack of ember-specific training resources.
Technology-Circuit-Algorithm Tri-Design for Processing-in-Pixel-in-Memory (P2M)
The massive amounts of data generated by camera sensors motivate data processing inside pixel arrays, i. e., at the extreme-edge.
ViTA: A Vision Transformer Inference Accelerator for Edge Applications
Vision Transformer models, such as ViT, Swin Transformer, and Transformer-in-Transformer, have recently gained significant traction in computer vision tasks due to their ability to capture the global relation between features which leads to superior performance.
Neuromorphic-P2M: Processing-in-Pixel-in-Memory Paradigm for Neuromorphic Image Sensors
Edge devices equipped with computer vision must deal with vast amounts of sensory data with limited computing resources.
In-Sensor & Neuromorphic Computing are all you need for Energy Efficient Computer Vision
Although the overhead for the first layer MACs with direct encoding is negligible for deep SNNs and the CV processing is efficient using SNNs, the data transfer between the image sensors and the downstream processing costs significant bandwidth and may dominate the total energy.
Hoyer regularizer is all you need for ultra low-latency spiking neural networks
Spiking Neural networks (SNN) have emerged as an attractive spatio-temporal computing paradigm for a wide range of low-power vision tasks.
Towards Energy-Efficient, Low-Latency and Accurate Spiking LSTMs
We obtain test accuracy of 94. 75% with only 2 time steps with direct encoding on the GSC dataset with 4. 1x lower energy than an iso-architecture standard LSTM.
Enabling ISP-less Low-Power Computer Vision
However, direct inference on the raw images degrades the test accuracy due to the difference in covariance of the raw images captured by the image sensors compared to the ISP-processed images used for training.
Self-Attentive Pooling for Efficient Deep Learning
With the aggressive down-sampling of the activation maps in the initial layers (providing up to 22x reduction in memory consumption), our approach achieves 1. 43% higher test accuracy compared to SOTA techniques with iso-memory footprints.
P2M-DeTrack: Processing-in-Pixel-in-Memory for Energy-efficient and Real-Time Multi-Object Detection and Tracking
The designs also reduce the sensor and total energy (obtained from in-house circuit simulations at Globalfoundries 22nm technology node) per frame by 5. 7x and 1. 14x, respectively.
Toward Efficient Hyperspectral Image Processing inside Camera Pixels
Hyperspectral cameras generate a large amount of data due to the presence of hundreds of spectral bands as opposed to only three channels (red, green, and blue) in traditional cameras.
P2M: A Processing-in-Pixel-in-Memory Paradigm for Resource-Constrained TinyML Applications
Visual data in such cameras are usually captured in the form of analog voltages by a sensor pixel array, and then converted to the digital domain for subsequent AI processing using analog-to-digital converters (ADC).
Can Deep Neural Networks be Converted to Ultra Low-Latency Spiking Neural Networks?
SOTA training strategies for SNNs involve conversion from a non-spiking deep neural network (DNN).
HYPER-SNN: Towards Energy-efficient Quantized Deep Spiking Neural Networks for Hyperspectral Image Classification
However, the accurate processing of the spectral and spatial correlation between the bands requires the use of energy-expensive 3-D Convolutional Neural Networks (CNNs).
Computational Efficiency
Hyperspectral Image Classification
+1
Training Energy-Efficient Deep Spiking Neural Networks with Single-Spike Hybrid Input Encoding
This paper presents a training framework for low-latency energy-efficient SNNs that uses a hybrid encoding scheme at the input layer in which the analog pixel values of an image are directly applied during the first timestep and a novel variant of spike temporal coding is used during subsequent timesteps.
Towards Low-Latency Energy-Efficient Deep SNNs via Attention-Guided Compression
To evaluate the merits of our approach, we performed experiments with variants of VGG and ResNet, on both CIFAR-10 and CIFAR-100, and VGG16 on Tiny-ImageNet. The SNN models generated through the proposed technique yield SOTA compression ratios of up to 33. 4x with no significant drops in accuracy compared to baseline unpruned counterparts.
