63 research outputs found
EmBench: Quantifying Performance Variations of Deep Neural Networks across Modern Commodity Devices
In recent years, advances in deep learning have resulted in unprecedented
leaps in diverse tasks spanning from speech and object recognition to context
awareness and health monitoring. As a result, an increasing number of
AI-enabled applications are being developed targeting ubiquitous and mobile
devices. While deep neural networks (DNNs) are getting bigger and more complex,
they also impose a heavy computational and energy burden on the host devices,
which has led to the integration of various specialized processors in commodity
devices. Given the broad range of competing DNN architectures and the
heterogeneity of the target hardware, there is an emerging need to understand
the compatibility between DNN-platform pairs and the expected performance
benefits on each platform. This work attempts to demystify this landscape by
systematically evaluating a collection of state-of-the-art DNNs on a wide
variety of commodity devices. In this respect, we identify potential
bottlenecks in each architecture and provide important guidelines that can
assist the community in the co-design of more efficient DNNs and accelerators.Comment: Accepted at MobiSys 2019: 3rd International Workshop on Embedded and
Mobile Deep Learning (EMDL), 201
Multi-Exit Semantic Segmentation Networks
Semantic segmentation arises as the backbone of many vision systems, spanning
from self-driving cars and robot navigation to augmented reality and
teleconferencing. Frequently operating under stringent latency constraints
within a limited resource envelope, optimising for efficient execution becomes
important. At the same time, the heterogeneous capabilities of the target
platforms and the diverse constraints of different applications require the
design and training of multiple target-specific segmentation models, leading to
excessive maintenance costs. To this end, we propose a framework for converting
state-of-the-art segmentation CNNs to Multi-Exit Semantic Segmentation (MESS)
networks: specially trained models that employ parametrised early exits along
their depth to i) dynamically save computation during inference on easier
samples and ii) save training and maintenance cost by offering a post-training
customisable speed-accuracy trade-off. Designing and training such networks
naively can hurt performance. Thus, we propose a novel two-staged training
scheme for multi-exit networks. Furthermore, the parametrisation of MESS
enables co-optimising the number, placement and architecture of the attached
segmentation heads along with the exit policy, upon deployment via exhaustive
search in <1 GPUh. This allows MESS to rapidly adapt to the device capabilities
and application requirements for each target use-case, offering a
train-once-deploy-everywhere solution. MESS variants achieve latency gains of
up to 2.83x with the same accuracy, or 5.33 pp higher accuracy for the same
computational budget, compared to the original backbone network. Lastly, MESS
delivers orders of magnitude faster architectural customisation, compared to
state-of-the-art techniques.Comment: (Extended version) Accepted at ECCV 202
LifeLearner: Hardware-Aware Meta Continual Learning System for Embedded Computing Platforms
Continual Learning (CL) allows applications such as user personalization and
household robots to learn on the fly and adapt to context. This is an important
feature when context, actions, and users change. However, enabling CL on
resource-constrained embedded systems is challenging due to the limited labeled
data, memory, and computing capacity. In this paper, we propose LifeLearner, a
hardware-aware meta continual learning system that drastically optimizes system
resources (lower memory, latency, energy consumption) while ensuring high
accuracy. Specifically, we (1) exploit meta-learning and rehearsal strategies
to explicitly cope with data scarcity issues and ensure high accuracy, (2)
effectively combine lossless and lossy compression to significantly reduce the
resource requirements of CL and rehearsal samples, and (3) developed
hardware-aware system on embedded and IoT platforms considering the hardware
characteristics. As a result, LifeLearner achieves near-optimal CL performance,
falling short by only 2.8% on accuracy compared to an Oracle baseline. With
respect to the state-of-the-art (SOTA) Meta CL method, LifeLearner drastically
reduces the memory footprint (by 178.7x), end-to-end latency by 80.8-94.2%, and
energy consumption by 80.9-94.2%. In addition, we successfully deployed
LifeLearner on two edge devices and a microcontroller unit, thereby enabling
efficient CL on resource-constrained platforms where it would be impractical to
run SOTA methods and the far-reaching deployment of adaptable CL in a
ubiquitous manner. Code is available at
https://github.com/theyoungkwon/LifeLearner.Comment: Accepted for publication at SenSys 202
Meta-Learned Kernel For Blind Super-Resolution Kernel Estimation
Recent image degradation estimation methods have enabled single-image
super-resolution (SR) approaches to better upsample real-world images. Among
these methods, explicit kernel estimation approaches have demonstrated
unprecedented performance at handling unknown degradations. Nonetheless, a
number of limitations constrain their efficacy when used by downstream SR
models. Specifically, this family of methods yields i) excessive inference time
due to long per-image adaptation times and ii) inferior image fidelity due to
kernel mismatch. In this work, we introduce a learning-to-learn approach that
meta-learns from the information contained in a distribution of images, thereby
enabling significantly faster adaptation to new images with substantially
improved performance in both kernel estimation and image fidelity.
Specifically, we meta-train a kernel-generating GAN, named MetaKernelGAN, on a
range of tasks, such that when a new image is presented, the generator starts
from an informed kernel estimate and the discriminator starts with a strong
capability to distinguish between patch distributions. Compared with
state-of-the-art methods, our experiments show that MetaKernelGAN better
estimates the magnitude and covariance of the kernel, leading to
state-of-the-art blind SR results within a similar computational regime when
combined with a non-blind SR model. Through supervised learning of an
unsupervised learner, our method maintains the generalizability of the
unsupervised learner, improves the optimization stability of kernel estimation,
and hence image adaptation, and leads to a faster inference with a speedup
between 14.24 to 102.1x over existing methods.Comment: Preprint: Accepted at the 2024 IEEE/CVF Winter Conference on
Applications of Computer Vision (WACV 2024
Dual practice in the health sector: review of the evidence
This paper reports on income generation practices among civil servants in the health sector, with a particular emphasis on dual practice. It first approaches the subject of public–private overlap. Thereafter it focuses on coping strategies in general and then on dual practice in particular. To compensate for unrealistically low salaries, health workers rely on individual coping strategies. Many clinicians combine salaried, public-sector clinical work with a fee-for-service private clientele. This dual practice is often a means by which health workers try to meet their survival needs, reflecting the inability of health ministries to ensure adequate salaries and working conditions. Dual practice may be considered present in most countries, if not all. Nevertheless, there is surprisingly little hard evidence about the extent to which health workers resort to dual practice, about the balance of economic and other motives for doing so, or about the consequences for the proper use of the scarce public resources dedicated to health. In this paper dual practice is approached from six different perspectives: (1) conceptual, regarding what is meant by dual practice; (2) descriptive, trying to develop a typology of dual practices; (3) quantitative, trying to determine its prevalence; (4) impact on personal income, the health care system and health status; (5) qualitative, looking at the reasons why practitioners so frequently remain in public practice while also working in the private sector and at contextual, personal life, institutional and professional factors that make it easier or more difficult to have dual practices; and (6) possible interventions to deal with dual practice
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