84 research outputs found
FedHIL: Heterogeneity Resilient Federated Learning for Robust Indoor Localization with Mobile Devices
Indoor localization plays a vital role in applications such as emergency
response, warehouse management, and augmented reality experiences. By deploying
machine learning (ML) based indoor localization frameworks on their mobile
devices, users can localize themselves in a variety of indoor and subterranean
environments. However, achieving accurate indoor localization can be
challenging due to heterogeneity in the hardware and software stacks of mobile
devices, which can result in inconsistent and inaccurate location estimates.
Traditional ML models also heavily rely on initial training data, making them
vulnerable to degradation in performance with dynamic changes across indoor
environments. To address the challenges due to device heterogeneity and lack of
adaptivity, we propose a novel embedded ML framework called FedHIL. Our
framework combines indoor localization and federated learning (FL) to improve
indoor localization accuracy in device-heterogeneous environments while also
preserving user data privacy. FedHIL integrates a domain-specific selective
weight adjustment approach to preserve the ML model's performance for indoor
localization during FL, even in the presence of extremely noisy data.
Experimental evaluations in diverse real-world indoor environments and with
heterogeneous mobile devices show that FedHIL outperforms state-of-the-art FL
and non-FL indoor localization frameworks. FedHIL is able to achieve 1.62x
better localization accuracy on average than the best performing FL-based
indoor localization framework from prior work
A Reinforcement Learning Framework with Region-Awareness and Shared Path Experience for Efficient Routing in Networks-on-Chip
Network-on-chip (NoC) architectures provide a scalable, high-performance, and
reliable interconnect for emerging manycore systems. The routing policies used
in NoCs have a significant impact on overall performance. Prior efforts have
proposed reinforcement learning (RL)-based adaptive routing policies to avoid
congestion and minimize latency in NoCs. The output quality of RL policies
depends on selecting a representative cost function and an effective update
mechanism. Unfortunately, existing RL policies for NoC routing fail to
represent path contention and regional congestion in the cost function.
Moreover, the experience of packet flows sharing the same route is not fully
incorporated into the RL update mechanism. In this paper, we present a novel
regional congestion-aware RL-based NoC routing policy called Q-RASP that is
capable of sharing experience from packets using the same routes. Q-RASP
improves average packet latency by up to 18.3% and reduces NoC energy
consumption by up to 6.7% with minimal area overheads compared to
state-of-the-art RL-based NoC routing implementations
CALLOC: Curriculum Adversarial Learning for Secure and Robust Indoor Localization
Indoor localization has become increasingly vital for many applications from
tracking assets to delivering personalized services. Yet, achieving pinpoint
accuracy remains a challenge due to variations across indoor environments and
devices used to assist with localization. Another emerging challenge is
adversarial attacks on indoor localization systems that not only threaten
service integrity but also reduce localization accuracy. To combat these
challenges, we introduce CALLOC, a novel framework designed to resist
adversarial attacks and variations across indoor environments and devices that
reduce system accuracy and reliability. CALLOC employs a novel adaptive
curriculum learning approach with a domain specific lightweight scaled-dot
product attention neural network, tailored for adversarial and variation
resilience in practical use cases with resource constrained mobile devices.
Experimental evaluations demonstrate that CALLOC can achieve improvements of up
to 6.03x in mean error and 4.6x in worst-case error against state-of-the-art
indoor localization frameworks, across diverse building floorplans, mobile
devices, and adversarial attacks scenarios
SHIELD: Sustainable Hybrid Evolutionary Learning Framework for Carbon, Wastewater, and Energy-Aware Data Center Management
Today's cloud data centers are often distributed geographically to provide
robust data services. But these geo-distributed data centers (GDDCs) have a
significant associated environmental impact due to their increasing carbon
emissions and water usage, which needs to be curtailed. Moreover, the energy
costs of operating these data centers continue to rise. This paper proposes a
novel framework to co-optimize carbon emissions, water footprint, and energy
costs of GDDCs, using a hybrid workload management framework called SHIELD that
integrates machine learning guided local search with a decomposition-based
evolutionary algorithm. Our framework considers geographical factors and
time-based differences in power generation/use, costs, and environmental
impacts to intelligently manage workload distribution across GDDCs and data
center operation. Experimental results show that SHIELD can realize 34.4x
speedup and 2.1x improvement in Pareto Hypervolume while reducing the carbon
footprint by up to 3.7x, water footprint by up to 1.8x, energy costs by up to
1.3x, and a cumulative improvement across all objectives (carbon, water, cost)
of up to 4.8x compared to the state-of-the-art
MOSAIC: A Multi-Objective Optimization Framework for Sustainable Datacenter Management
In recent years, cloud service providers have been building and hosting
datacenters across multiple geographical locations to provide robust services.
However, the geographical distribution of datacenters introduces growing
pressure to both local and global environments, particularly when it comes to
water usage and carbon emissions. Unfortunately, efforts to reduce the
environmental impact of such datacenters often lead to an increase in the cost
of datacenter operations. To co-optimize the energy cost, carbon emissions, and
water footprint of datacenter operation from a global perspective, we propose a
novel framework for multi-objective sustainable datacenter management (MOSAIC)
that integrates adaptive local search with a collaborative decomposition-based
evolutionary algorithm to intelligently manage geographical workload
distribution and datacenter operations. Our framework sustainably allocates
workloads to datacenters while taking into account multiple geography- and
time-based factors including renewable energy sources, variable energy costs,
power usage efficiency, carbon factors, and water intensity in energy. Our
experimental results show that, compared to the best-known prior work
frameworks, MOSAIC can achieve 27.45x speedup and 1.53x improvement in Pareto
Hypervolume while reducing the carbon footprint by up to 1.33x, water footprint
by up to 3.09x, and energy costs by up to 1.40x. In the simultaneous
three-objective co-optimization scenario, MOSAIC achieves a cumulative
improvement across all objectives (carbon, water, cost) of up to 4.61x compared
to the state-of-the-arts
Design Space Exploration for PCM-based Photonic Memory
The integration of silicon photonics (SiPh) and phase change materials (PCMs)
has created a unique opportunity to realize adaptable and reconfigurable
photonic systems. In particular, the nonvolatile programmability in PCMs has
made them a promising candidate for implementing optical memory systems. In
this paper, we describe the design of an optical memory cell based on PCMs
while exploring the design space of the cell in terms of PCM material choice
(e.g., GST, GSST, Sb2Se3), cell bit capacity, latency, and power consumption.
Leveraging this design-space exploration for the design of efficient optical
memory cells, we present the design and implementation of an optical memory
array and explore its scalability and power consumption when using different
optical memory cells. We also identify performance bottlenecks that need to be
alleviated to further scale optical memory arrays with competitive latency and
energy consumption, compared to their electronic counterparts.Comment: This paper will appear in the proceedings of ACM GLSVLSI 202
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