Koopman theory plus knowledge distillation yields linearized models from pre-trained nets that outperform standard least-squares Koopman approximations on MNIST and Fashion-MNIST in accuracy and stability.
Pho- tonics for artificial intelligence and neuromorphic comput ing
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Clockless FPGA circuits produce autonomous spiking neuron networks that achieve competitive audio classification accuracy with significantly lower power than conventional digital implementations.
An experimental 4-channel TSWDM silicon photonic crossbar achieves 0.96 TOPS for hyperdimensional tensor operations with 3.9% average error and 93.3% Iris accuracy at 10-30 GBd.
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Extraction of linearized models from pre-trained networks via knowledge distillation
Koopman theory plus knowledge distillation yields linearized models from pre-trained nets that outperform standard least-squares Koopman approximations on MNIST and Fashion-MNIST in accuracy and stability.
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Scalable neuromorphic computing from autonomous spiking dynamics in a clockless reconfigurable chip
Clockless FPGA circuits produce autonomous spiking neuron networks that achieve competitive audio classification accuracy with significantly lower power than conventional digital implementations.
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On-chip 1 TOPS Hyperdimensional Photonic Tensor Core using a WDM Silicon Photonic Coherent Crossbar
An experimental 4-channel TSWDM silicon photonic crossbar achieves 0.96 TOPS for hyperdimensional tensor operations with 3.9% average error and 93.3% Iris accuracy at 10-30 GBd.
- Beyond Silicon: Materials, Mechanisms, and Methods for Physical Neural Computing