Resonant machine learning algorithms, architecture and applications
AssigneeWashington University
InventorsShantanu Chakrabartty, Oindrila Chatterjee
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Abstract
Devices, systems, and methods related to an energy-efficient machine learning framework which exploits structural and functional similarities between a machine learning network and a general electrical network satisfying the Tellegen's theorem are described.
Claims13
A resonant machine learning processor comprising a network of internal nodes, each internal node comprising an LC tank, each LC tank comprising a variable capacitor with capacitance Ci connected electrically in parallel with a variable inductor with inductance Li, each internal node electrically connected to a ground node and to each remaining internal node of the network of internal nodes, each node further comprising a normalized voltage phasor Vi, a normalized current phasor Ii, and a phase angle ϕi defined across each node of the network of internal nodes, wherein:
- a. during learning a plurality of learning parameters, the capacitance Ci and the inductance Li of each node is modulated to adjust a relative magnitude of the normalized voltage phasor Vi and the normalized current phasor Ii to optimize a total active network power and to maintain a total network reactive network power at essentially zero until a steady state network resonance is achieved;
- and
- b. after completion of learning, the steady state network resonance is maintained, and the plurality of learned network parameters are stored and sustained using resonant magnetic fields and resonant magnetic fields produced within the LC tanks of the network in internal nodes;
- wherein the steady state network resonance is maintained without dissipating power.
A resonant machine-learning network system comprising a computing device, the computing device comprising at least one processor and a memory storing a plurality of modules, each module comprising instructions executable on the at least one processor, the plurality of modules comprising:
- a. a resonant machine learning network module to define a plurality of interconnected nodes, wherein each node comprises a normalized voltage phasor Vi, a normalized current phasor Ii, and a phase angle ϕi defined across each node of the plurality of nodes;
- b. a complex growth transform module to update the plurality of nodes according to complex growth transform model;
- and
- c. a resonant network convergence module to converge the plurality of nodes to a steady state solution by optimizing a system objective function subject to at least one constraint.
Dependencies are inferred from the claim text.