Periodic Reporting for period 1 - PRICOM (Printed Computing: Enabling Extremely Low Cost Pervasive Near Sensor Computing)
Berichtszeitraum: 2022-10-01 bis 2025-03-31
Computing has yet to penetrate cost-sensitive domains such as fast-moving consumer goods and personalized medicine, largely due to the high production costs of rigid silicon technologies. Additive manufacturing for printed electronics offers a low-cost, flexible alternative, but conventional digital architectures are unsuitable due to limited device counts, large feature sizes, and high variability in printed components.
The PRICOM project develops unconventional mixed-signal classifier paradigms optimized for additive printing and flexible electronics, aiming to minimize hardware footprint while directly processing analog sensory inputs with high classification accuracy. Key contributions include:
1. Printed Mixed-Signal Classifiers – Novel architectures and tailored training algorithms that process analog sensory data with far smaller area than digital counterparts, maintaining accuracy.
2. Design Space Exploration – Hardware-in-the-loop optimization of printed ML primitives, balancing accuracy, reliability, area, energy, and performance.
3. Reliable Physical Design – Data-driven layout rules and variation-aware algorithms to improve yield and reliability under manufacturing variability.
4. In-Situ Post-Fabrication Tuning – Iterative tuning to correct imperfections and enable point-of-use customization.
5.Analog Function Approximation – First framework using analog building blocks for compact, low-power computation in flexible electronics.
PRICOM advances computing from materials and devices to complete systems, enabling low-cost, pervasive computing in emerging domains.
The PRICOM project develops unconventional mixed-signal classifier paradigms optimized for additive printing and flexible electronics, aiming to minimize hardware footprint while directly processing analog sensory inputs with high classification accuracy. Key contributions include:
1. Printed Mixed-Signal Classifiers – Novel architectures and tailored training algorithms that process analog sensory data with far smaller area than digital counterparts, maintaining accuracy.
2. Design Space Exploration – Hardware-in-the-loop optimization of printed ML primitives, balancing accuracy, reliability, area, energy, and performance.
3. Reliable Physical Design – Data-driven layout rules and variation-aware algorithms to improve yield and reliability under manufacturing variability.
4. In-Situ Post-Fabrication Tuning – Iterative tuning to correct imperfections and enable point-of-use customization.
5.Analog Function Approximation – First framework using analog building blocks for compact, low-power computation in flexible electronics.
PRICOM advances computing from materials and devices to complete systems, enabling low-cost, pervasive computing in emerging domains.
The PRICOM project has delivered multiple breakthroughs in printed and flexible electronics for machine learning and neuromorphic computing, advancing beyond the state-of-the-art:
1. Analog Printed Spiking Neuromorphic Circuits – First fully functional spiking neuromorphic system using inorganic printed electrolyte-gated transistors (EGTs), enabling energy-efficient processing on flexible substrates.
2. Cost-Effective On-Sensor Learning – Reduced ADC front-end cost and complexity for training printed MLPs, improving scalability and resource efficiency.
3. Approximate Computing for Printed Circuits – Co-design methodology for approximate MLPs that optimizes power, area, and reliability while preserving high accuracy.
4. Model-to-Circuit Cross-Approximation – Automated framework combining algorithmic approximation, logic pruning, and voltage over-scaling to cut area and power without major accuracy loss.
5. Bespoke Robust Printed Neuromorphic Circuits – Custom designs with device-level optimizations for stable operation under manufacturing variability.
6. SpikeSynth – Adaptive analog printed spiking neural network framework achieving robust, low-power computation through device-level adaptability.
7. PRINT-SAFE – Ultra-low-cost printed electronic design with scalable adaptive fault endurance for resilience under variations and stress.
8. Automatic Test Pattern Generation – Framework for efficient fault detection in printed neuromorphic circuits, tailored to variability constraints.
9. Low-Power Flexible Stress Monitoring – Energy-efficient classifiers for continuous wearable stress monitoring on flexible substrates.
10. Binary Search ADC for Flexible Classifiers – First co-designed binary search ADC with in-training calibration to improve accuracy and efficiency under device variability.
11. Analog Function Approximation – First framework using analog building blocks in flexible electronics for compact, low-power computation.
These innovations establish a new paradigm in printed computing, addressing energy efficiency, cost, reliability, and ML optimization for low-cost, flexible, and power-constrained applications.
1. Analog Printed Spiking Neuromorphic Circuits – First fully functional spiking neuromorphic system using inorganic printed electrolyte-gated transistors (EGTs), enabling energy-efficient processing on flexible substrates.
2. Cost-Effective On-Sensor Learning – Reduced ADC front-end cost and complexity for training printed MLPs, improving scalability and resource efficiency.
3. Approximate Computing for Printed Circuits – Co-design methodology for approximate MLPs that optimizes power, area, and reliability while preserving high accuracy.
4. Model-to-Circuit Cross-Approximation – Automated framework combining algorithmic approximation, logic pruning, and voltage over-scaling to cut area and power without major accuracy loss.
5. Bespoke Robust Printed Neuromorphic Circuits – Custom designs with device-level optimizations for stable operation under manufacturing variability.
6. SpikeSynth – Adaptive analog printed spiking neural network framework achieving robust, low-power computation through device-level adaptability.
7. PRINT-SAFE – Ultra-low-cost printed electronic design with scalable adaptive fault endurance for resilience under variations and stress.
8. Automatic Test Pattern Generation – Framework for efficient fault detection in printed neuromorphic circuits, tailored to variability constraints.
9. Low-Power Flexible Stress Monitoring – Energy-efficient classifiers for continuous wearable stress monitoring on flexible substrates.
10. Binary Search ADC for Flexible Classifiers – First co-designed binary search ADC with in-training calibration to improve accuracy and efficiency under device variability.
11. Analog Function Approximation – First framework using analog building blocks in flexible electronics for compact, low-power computation.
These innovations establish a new paradigm in printed computing, addressing energy efficiency, cost, reliability, and ML optimization for low-cost, flexible, and power-constrained applications.
Printed Spiking Neural Networks
Biologically inspired Spiking Neural Networks (SNNs) offer energy-efficient neuromorphic computing for emerging domains such as soft robotics, wearables, and IoT. Printed electronics, leveraging soft materials and flexible substrates, provides a low-cost alternative to silicon.
We present the first complete spiking neuromorphic computing system implemented using inorganic printed electronics, featuring:
1. Programmable Energy-Efficient Spiking Neuron – N-type electrolyte-gated transistor (EGT)-based neuron optimized for low-voltage, energy-harvested edge applications.
2. Transformer-Based Learning Model – Differential Transformer architecture for task-specific training, ensuring adaptability and accuracy.
3. Performance Validation – Simulation-based validation and benchmarking on standard datasets, confirming efficiency and applicability.
4. Variation aware modeling - Robustness aware training of proposed SNN
This interdisciplinary approach merges neuromorphic computing, printed electronics, and ML to enable ultra-low-cost, energy-efficient systems for resource-constrained environments.
Model-to-Circuit Cross-Approximation for Printed Classifiers
We introduce the first automated cross-layer approximation framework for printed ML classifiers (MLPs, SVMs), combining:
a) Algorithm-level coefficient approximation,
b)Logic-level netlist pruning,
c) Circuit-level voltage over-scaling.
Evaluations on 12 MLPs and 12 SVMs (6000+ designs) show 51% area and 66% power reduction with <5% accuracy loss. Notably, 80% of classifiers run on battery power while maintaining near-identical accuracy to exact designs.
Key contributions:
1. First Holistic Cross-Approximation Evaluation for printed ML classifier design.
2. Automated Optimal Design Generation under battery constraints.
3.Battery-Powered Approximate Computing enabling complex, low-power printed ML classifiers.
Biologically inspired Spiking Neural Networks (SNNs) offer energy-efficient neuromorphic computing for emerging domains such as soft robotics, wearables, and IoT. Printed electronics, leveraging soft materials and flexible substrates, provides a low-cost alternative to silicon.
We present the first complete spiking neuromorphic computing system implemented using inorganic printed electronics, featuring:
1. Programmable Energy-Efficient Spiking Neuron – N-type electrolyte-gated transistor (EGT)-based neuron optimized for low-voltage, energy-harvested edge applications.
2. Transformer-Based Learning Model – Differential Transformer architecture for task-specific training, ensuring adaptability and accuracy.
3. Performance Validation – Simulation-based validation and benchmarking on standard datasets, confirming efficiency and applicability.
4. Variation aware modeling - Robustness aware training of proposed SNN
This interdisciplinary approach merges neuromorphic computing, printed electronics, and ML to enable ultra-low-cost, energy-efficient systems for resource-constrained environments.
Model-to-Circuit Cross-Approximation for Printed Classifiers
We introduce the first automated cross-layer approximation framework for printed ML classifiers (MLPs, SVMs), combining:
a) Algorithm-level coefficient approximation,
b)Logic-level netlist pruning,
c) Circuit-level voltage over-scaling.
Evaluations on 12 MLPs and 12 SVMs (6000+ designs) show 51% area and 66% power reduction with <5% accuracy loss. Notably, 80% of classifiers run on battery power while maintaining near-identical accuracy to exact designs.
Key contributions:
1. First Holistic Cross-Approximation Evaluation for printed ML classifier design.
2. Automated Optimal Design Generation under battery constraints.
3.Battery-Powered Approximate Computing enabling complex, low-power printed ML classifiers.