  The high demand for machine intelligence of doubling every three months is
driving novel hardware solutions beyond charging of electrical wires given a
resurrection to application specific integrated circuit (ASIC)-based
accelerators. These innovations include photonic ASICs (P-ASIC) due to
prospects of performing optical linear (and also nonlinear) operations, such as
multiply-accumulate for vector matrix multiplications or convolutions, without
iterative architectures. Such photonic linear algebra enables picosecond delay
when photonic integrated circuits are utilized, via on-the-fly mathematics.
However, the neurons full function includes providing a nonlinear activation
function, knowns as thresholding, to enable decision making on inferred data.
Many P-ASIC solutions performing this nonlinearity in the electronic domain,
which brings challenges in terms of data throughput and delay, thus breaking
the optical link and introducing increased system complexity via domain
crossings. This work follows the notion of utilizing enhanced
light-matter-interactions to provide efficient, compact, and engineerable
electro-optic neuron nonlinearity. Here, we introduce and demonstrate a novel
electro-optic device to engineer the shape of this optical nonlinearity to
resemble a rectifying linear unit (ReLU) - the most-commonly used nonlinear
activation function in neural networks. We combine the counter-directional
transfer functions from heterostructures made out of two electro-optic
materials to design a diode-like nonlinear response of the device. Integrating
this nonlinearity into a photonic neural network, we show how the
electrostatics of this thresholders gating junction improves machine learning
inference accuracy and the energy efficiency of the neural network.
