The computer industry is witnessing an inflection point – ‘Internet of Things combined with Cloud Analytics’ –which has implications from end (sensor devices) to end (cloud architectures). Many technologies come together contributing to this major inflection point: Computing platforms getting smaller (e.g. handheld devices, wearables), richer (e.g. image and language understanding) and broader (i.e. reaching the masses via Internet of Things). Sensors operating in constrained environments connected through intelligent gateways and cloud backend creates a very complex environment for the operators, system integrators, and developers of this new emerging technology. Discovering and managing sensor devices; collecting, cleaning and storing discoverable data; normalizing, aggregating and analyzing the data for insights and actions; managing the security and privacy of the data, enforcing the access privileges and trusted execution environments – all these are required to make this revolution happen.
The research challenges in IoT platforms are multi-fold:
(a) providing rich functionality and wider power/performance range for sensor devices
(b) attempting to cover a broad range of applications that can be migrated from cloud to gateways and sensor devices
(c) enabling a scalable and modular cloud architecture that provides the required real-time and uptime capabilities
(d) providing a rich software programming environment that eases the challenge of developing applications on end to end platforms consisting of elements ranging from sensors to gateways to cloud.
The goal of this workshop is to bring together academic researchers and industry practitioners to discuss future IoT sensor-to- cloud architectures including sensors, gateways and cloud architectures.
Below is the proposed list of topics for the workshop. Topics include, but are not restricted to, the following:
Sensors, Actuators, Gateway & Controllers Architectures
Architectures for wearable and IOT devices
Heterogeneity in Cores, Frequency, Cache, Memory
Power, Performance, Energy optimizations
SoCs, CPU/GPU, CPU/GPGPU architectures
Ultra-Low Power Core Micro-architectures
Fabrics / Network-on-chip, Cache/Memory Hierarchies
HW Support for Heterogeneity, Programmability, Modularity
Simulation / Emulation Methodologies
Protocols and abstraction layers (MQTT, CoAP, REST, …)
Cloud Architecture
Data Center Architectures for IoT; customization and specialization
Edge/Fog computing Dynamic Cloud-gateway-device offloads
Workload/Algorithm Partitioning between Heterogeneous Cores and Accelerators
BigData Frameworks (Hadoop, Spark, Flink, …)
Heterogeneous Datacenters (FPGA, GPU, Accelerators, …)
Machine Learning Algorithms & Applications, Graph processing, Deep Neural Networks
Batch, streaming and distributed Analytics
Design Patterns and Application Programming frameworks
Emerging Workloads and Use cases
Wearable and IOT use cases and workloads
Speech/Image recognition and understanding, Cognitive computing
Personal Assistants, Predictive/Prescriptive Analytics, Robotics
Workload Analysis for power/performance/energy optimization and acceleration
Performance Monitoring and Simulation, Architecture analysis
Novel Accelerator Designs
Specialized Accelerator Architectures and Designs
Machine Learning, Neural Network and Graph Processing accelerators
Domain-Specific Programmable/Configurable Accelerators
Accelerator Interfaces for Programmability
Development Environments for Accelerator Design
02月04日
2017
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