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Various hardware accelerators have been developed for energy-efficient and real-time inference of neural networks on edge devices. However, most training is done on high-performance GPUs or servers, and the huge memory and computing costs prevent training neural networks on edge devices. This paper proposes a novel tensor-based training framework, which offers orders-of-magnitude memory reduction in the training process. We propose a novel rank-adaptive tensorized neural network model, and design a hardware-friendly low-precision algorithm to train this model. We present an FPGA accelerator to demonstrate the benefits of this training method on edge devices. Our preliminary FPGA implementation achieves 59× speedup and 123× energy reduction compared to embedded CPU, and 292× memory reduction over a standard full-size training.

Due to environmental concerns and the increasing drive towards miniaturization of electronic circuits and devices, lead-free ferroelectric films with low leakage current and robust ferroelectric and piezoelectric properties are highly desired. The preferred alternative, BaTiO 3 , is non-toxic and has ferroelectric properties, but its high leakage current, poor ferroelectricity and piezoelectricity and low Curie temperature of ∼130 °C in thin film form are obstacles for high-temperature practical applications. Here, we report that a negative-pressure-driven enhancement of ferroelectric Curie temperature and effective piezoelectric coefficient are achieved in (111)-oriented BaTiO 3 nanocomposite films. The enhanced ferroelectric and piezoelectric properties in the emergent monoclinic BaTiO 3 are attributed to the sharp vertical interface and 3D tensile strain that develops upon interspersing stiff and self-assembled vertical Sm 2 O 3 nanopillars through the film thickness. Our work also demonstrates that fabricating oxide films through (111)-oriented epitaxy opens up new avenues for the creation of new phase components and exploration of novel functionalities for developing oxide quantum electronic devices.