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Found 9 papers, 1 papers with code
Inter-Layer Scheduling Space Exploration for Multi-model Inference on Heterogeneous Chiplets
To address increasing compute demand from recent multi-model workloads with heavy models like large language models, we propose to deploy heterogeneous chiplet-based multi-chip module (MCM)-based accelerators.
MaGNAS: A Mapping-Aware Graph Neural Architecture Search Framework for Heterogeneous MPSoC Deployment
To achieve this, MaGNAS employs a two-tier evolutionary search to identify optimal GNNs and mapping pairings that yield the best performance trade-offs.
SEO: Safety-Aware Energy Optimization Framework for Multi-Sensor Neural Controllers at the Edge
Runtime energy management has become quintessential for multi-sensor autonomous systems at the edge for achieving high performance given the platform constraints.
Map-and-Conquer: Energy-Efficient Mapping of Dynamic Neural Nets onto Heterogeneous MPSoCs
Heterogeneous MPSoCs comprise diverse processing units of varying compute capabilities.
EnergyShield: Provably-Safe Offloading of Neural Network Controllers for Energy Efficiency
To mitigate the high energy demand of Neural Network (NN) based Autonomous Driving Systems (ADSs), we consider the problem of offloading NN controllers from the ADS to nearby edge-computing infrastructure, but in such a way that formal vehicle safety properties are guaranteed.
HADAS: Hardware-Aware Dynamic Neural Architecture Search for Edge Performance Scaling
Dynamic neural networks (DyNNs) have become viable techniques to enable intelligence on resource-constrained edge devices while maintaining computational efficiency.
Romanus: Robust Task Offloading in Modular Multi-Sensor Autonomous Driving Systems
Due to the high performance and safety requirements of self-driving applications, the complexity of modern autonomous driving systems (ADS) has been growing, instigating the need for more sophisticated hardware which could add to the energy footprint of the ADS platform.
SAGE: A Split-Architecture Methodology for Efficient End-to-End Autonomous Vehicle Control
We evaluate SAGE using an Nvidia Jetson TX2 and an industry-standard Nvidia Drive PX2 as the AV edge devices and demonstrate that our offloading strategy is practical for a wide range of DL models and internet connection bandwidths on 3G, 4G LTE, and WiFi technologies.
LENS: Layer Distribution Enabled Neural Architecture Search in Edge-Cloud Hierarchies
Edge-Cloud hierarchical systems employing intelligence through Deep Neural Networks (DNNs) endure the dilemma of workload distribution within them.
