Cluster 6.3.1:   Utility maximization

Task 6.3.1.1. -- Utility maximization for microscopic sensing system design

A general methodology for joint design optimization of all system components, parameters, and algorithms maximizing end-to-end system utility within energy and size constraints will be developed.

Task 6.3.1.2. -- Run-time dynamic system optimization via utility maximization

Stochastic utility maximization will be applied to dynamic run-time optimization of scalable system parameters to maximize lifetime total expected utility within energy, bandwidth, and other conservable and non-conservable system resources.

Task 6.3.1.3. -- Utility metrics for microscopic sensing system applications

New classes of utility metrics relevant to microscopic sensing systems will be developed: e.g. utilityweighted mean-squared error, event detection probability, and response-time-weighted metrics.

Cluster 6.3.2:   Attention-optimized multi-scale systems

Task 6.3.2.1. -- Stochastic feedback control methods for multiscale systems

Low-cost dynamic system-adjustment algorithms based on feedback and inhibition Optimal feedback delivery paradigms based on stochastic control and feedback information theory

Task 6.3.2.2. -- Stochastic feedback control methods for multiscale systems

We will develop efficient adaptive mechanisms to control the attention and selection of sensors.

Task 6.3.2.4. -- Real-Time information flow management

We will develop methods of capturing the utility of information and use it via feedback to improve performance, taking into account the global topology of the information flow, and retransmissions.

Task 6.3.2.5. -- Hugely scalable adjustable-attention signal-processing algorithms

Multiscale hierarchical distributed detection algorithms scalable across several orders of magnitude in computation/power-consumption Signal detection and -sorting methods for brain-machine interface applications scalable across several orders of magnitude in computation/power-consumption Attention-adaptive joint sensing and processing strategies for energy/performance management

Task 6.3.2.6. -- Population coding-inspired Stochastic Computing

Population coding applied to the development of robust stochastic computation techniques

Cluster 6.3.3:   Hugely scalable platforms for microscopic systems

Task 6.3.3.1. -- Platform strategy for ULE microscopic systems

We will develop a re-usable and modular platform strategy for ultra-low energy (ULE) microscopic systems. This would include libraries of scalable components, and a composition and integration methodology.

Task 6.3.3.2. -- Integrated 3D packaging for microscopic systems

Energy-efficiency-optimized 3D system integration and packaging for heterogeneous integration Ultra-dense, ultra-low-power cross-talk-minimizing packaging approaches Jointly optimized 3D packaging for energy-harvesting microscopic sensing systems

Task 6.3.3.3. -- Hugely power/performance-scalable system design

Processing systems that achieve near-optimal performance across orders-of-magnitude scaling (subkHz to 100's of MHz) Ultra-pipelined signal-processing implementations operating below 250mV for significantly improved energy efficiency

Task 6.3.3.4. -- Hugely-scalable ULE RF wireless links

Ultra-low-energy pulse-based proximity communication for implantable applications End-to-end trade-off analysis of hugely scalable wireless link options

Task 6.3.3.5. -- Energy scavenging and wireless power for microscale devices

Optimal distributed RF remote-power solutions for microscale sensor nodes (over varying node sizes and communication environment) Complete end-to-end energy-harvesting system including energy conversion and storage, based on distributed system-level adaptive energy management strategy.

Task 6.3.3.6. -- Microscale distributed sensors

Explore and develop concepts of passive embedded sensor arrays Distributed RF interrogation technology for addressing and sensing from distributed microsphere array Ultra-efficient RF array design and power management for passive distributed microsensor arrays

Cluster 6.3.4:   Multi-Scale Small-Scale Sensing System Demonstrators

Task 6.3.4.1. -- Microscopic system platform demonstrator

The following elements will be included in this multi-scale BMI system integration: A task-specific, dynamic utility metric that adjusts to the varying accuracy, precision, and latency requirements of a deployed BMI A stochastic adaptive feedback control algorithm that dynamically optimizes system performance within bandwidth and energy constraints and adjusts itself as a result of learning and adaptation Personal-area network management with dynamic attentional adaptation An end-to-end experimental BMI system employing a variety of sensory inputs at different resolutions controlling diverse actuators (prosthetics and micro-stimulators)

Task 6.3.4.2. -- Enhanced human-centric microscopic platform demonstrator

We will develop an ultra-low-energy brain-machine system instance that monitors a distributed array of microsphere neural firing sensors, performs scalable local processing at the monitoring microscopic implant, and transmits an optimized information stream to an array of interrogators. A microscopic prototype system will be integrated combining both fabricated and off-the shelf components (as resulting from the previous tasks). A joint optimization over the complete system space will be performed, guaranteeing the absolute lowest possible energy consumption in correspondence with demanded functionality. System elements include hugely performance-scalable processor, stochastic scalable co-processor (in collaboration with GSRC), passive micro-sensors and array-based RF interrogation, hugely scalable RF communication link, remote powering system, and system-optimized packaging solution