16 research outputs found
Self-Attentive Pooling for Efficient Deep Learning
Efficient custom pooling techniques that can aggressively trim the dimensions
of a feature map and thereby reduce inference compute and memory footprint for
resource-constrained computer vision applications have recently gained
significant traction. However, prior pooling works extract only the local
context of the activation maps, limiting their effectiveness. In contrast, we
propose a novel non-local self-attentive pooling method that can be used as a
drop-in replacement to the standard pooling layers, such as max/average pooling
or strided convolution. The proposed self-attention module uses patch
embedding, multi-head self-attention, and spatial-channel restoration, followed
by sigmoid activation and exponential soft-max. This self-attention mechanism
efficiently aggregates dependencies between non-local activation patches during
down-sampling. Extensive experiments on standard object classification and
detection tasks with various convolutional neural network (CNN) architectures
demonstrate the superiority of our proposed mechanism over the state-of-the-art
(SOTA) pooling techniques. In particular, we surpass the test accuracy of
existing pooling techniques on different variants of MobileNet-V2 on ImageNet
by an average of 1.2%. 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. This enables the deployment of our models in
memory-constrained devices, such as micro-controllers (without losing
significant accuracy), because the initial activation maps consume a
significant amount of on-chip memory for high-resolution images required for
complex vision tasks. Our proposed pooling method also leverages the idea of
channel pruning to further reduce memory footprints.Comment: 9 pages, 4 figures, conferenc
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. This paper
presents a comprehensive Deep Learning (DL) based on-line detection-correction
approach, suitable for a wide range of pixel corruption rates. A confidence
calibrated segmentation approach is introduced, which achieves nearly perfect
bad pixel detection, even with few training samples. A computationally
light-weight correction algorithm is proposed for low rates of pixel
corruption, that surpasses the accuracy of traditional interpolation-based
techniques. We also propose an autoencoder based image reconstruction approach
which alleviates the need for prior bad pixel detection and yields promising
results for high rates of pixel corruption. Unlike previous methods, which use
proprietary images, we demonstrate the efficacy of the proposed methods on the
open-source Samsung S7 ISP and MIT-Adobe FiveK datasets. Our approaches yield
up to 99.6% detection accuracy with <0.6% false positives and corrected images
within 1.5% average pixel error from 70% corrupted images
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. Hence, researchers have been
exploring different energy-efficient solutions such as near-sensor processing,
in-sensor processing, and in-pixel processing, bringing the computation closer
to the sensor. In particular, in-pixel processing embeds the computation
capabilities inside the pixel array and achieves high energy efficiency by
generating low-level features instead of the raw data stream from CMOS image
sensors. Many different in-pixel processing techniques and approaches have been
demonstrated on conventional frame-based CMOS imagers, however, the
processing-in-pixel approach for neuromorphic vision sensors has not been
explored so far. In this work, we for the first time, propose an asynchronous
non-von-Neumann analog processing-in-pixel paradigm to perform convolution
operations by integrating in-situ multi-bit multi-channel convolution inside
the pixel array performing analog multiply and accumulate (MAC) operations that
consume significantly less energy than their digital MAC alternative. To make
this approach viable, we incorporate the circuit's non-ideality, leakage, and
process variations into a novel hardware-algorithm co-design framework that
leverages extensive HSpice simulations of our proposed circuit using the GF22nm
FD-SOI technology node. We verified our framework on state-of-the-art
neuromorphic vision sensor datasets and show that our solution consumes ~2x
lower backend-processor energy while maintaining almost similar front-end
(sensor) energy on the IBM DVS128-Gesture dataset than the state-of-the-art
while maintaining a high test accuracy of 88.36%.Comment: 17 pages, 11 figures, 2 table
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. Several critical
developments have fueled recent interest in the processing-in-pixel-in-memory
paradigm for a wide range of visual machine intelligence tasks, including (1)
advances in 3D integration technology to enable complex processing inside each
pixel in a 3D integrated manner while maintaining pixel density, (2) analog
processing circuit techniques for massively parallel low-energy in-pixel
computations, and (3) algorithmic techniques to mitigate non-idealities
associated with analog processing through hardware-aware training schemes. This
article presents a comprehensive technology-circuit-algorithm landscape that
connects technology capabilities, circuit design strategies, and algorithmic
optimizations to power, performance, area, bandwidth reduction, and
application-level accuracy metrics. We present our results using a
comprehensive co-design framework incorporating hardware and algorithmic
optimizations for various complex real-life visual intelligence tasks mapped
onto our P2M paradigm
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. Hence, researchers have been exploring different energy-efficient solutions such as near-sensor, in-sensor, and in-pixel processing, bringing the computation closer to the sensor. In particular, in-pixel processing embeds the computation capabilities inside the pixel array and achieves high energy efficiency by generating low-level features instead of the raw data stream from CMOS image sensors. Many different in-pixel processing techniques and approaches have been demonstrated on conventional frame-based CMOS imagers; however, the processing-in-pixel approach for neuromorphic vision sensors has not been explored so far. In this work, for the first time, we propose an asynchronous non-von-Neumann analog processing-in-pixel paradigm to perform convolution operations by integrating in-situ multi-bit multi-channel convolution inside the pixel array performing analog multiply and accumulate (MAC) operations that consume significantly less energy than their digital MAC alternative. To make this approach viable, we incorporate the circuit's non-ideality, leakage, and process variations into a novel hardware-algorithm co-design framework that leverages extensive HSpice simulations of our proposed circuit using the GF22nm FD-SOI technology node. We verified our framework on state-of-the-art neuromorphic vision sensor datasets and show that our solution consumes ~2× lower backend-processor energy while maintaining almost similar front-end (sensor) energy on the IBM DVS128-Gesture dataset than the state-of-the-art while maintaining a high test accuracy of 88.36%
Integrating transcriptomic and proteomic data for accurate assembly and annotation of genomes
© 2017 Wong et al.; Published by Cold Spring Harbor Laboratory Press. Complementing genome sequence with deep transcriptome and proteome data could enable more accurate assembly and annotation of newly sequenced genomes. Here, we provide a proof-of-concept of an integrated approach for analysis of the genome and proteome of Anopheles stephensi, which is one of the most important vectors of the malaria parasite. To achieve broad coverage of genes, we carried out transcriptome sequencing and deep proteome profiling of multiple anatomically distinct sites. Based on transcriptomic data alone, we identified and corrected 535 events of incomplete genome assembly involving 1196 scaffolds and 868 protein-coding gene models. This proteogenomic approach enabled us to add 365 genes that were missed during genome annotation and identify 917 gene correction events through discovery of 151 novel exons, 297 protein extensions, 231 exon extensions, 192 novel protein start sites, 19 novel translational frames, 28 events of joining of exons, and 76 events of joining of adjacent genes as a single gene. Incorporation of proteomic evidence allowed us to change the designation of more than 87 predicted noncoding RNAs to conventional mRNAs coded by protein-coding genes. Importantly, extension of the newly corrected genome assemblies and gene models to 15 other newly assembled Anopheline genomes led to the discovery of a large number of apparent discrepancies in assembly and annotation of these genomes. Our data provide a framework for how future genome sequencing efforts should incorporate transcriptomic and proteomic analysis in combination with simultaneous manual curation to achieve near complete assembly and accurate annotation of genomes