**1. Introduction**

Von Neumann digital computer architecture [1] that existed since the 1940s and still being the only viable architecture for computers cannot keep up with the exponential speed needed, to process data for machine learning and artificial intelligence applications as we move into a internet of things (IoT) dominated world. This architecture cannot keep up with Moore's law that predicted the count of transistors in a CPU to double every 2 years, while at the same time, the CPU clock rate reached a ceiling at 4 GHz due to prevalence of current leakage in nanometric nodes. Hence, the move to multicore architecture is running against the power requirements for simultaneously powering these cores. All this can be traced to the excess amount of energy consumption of digital switching and the bandwidth limitation of the metal interconnects. These listed bottlenecks are driving the efforts for a new computing architecture towards the use of neuromorphic photonics, especially with the fast track to maturity that photonic integration has taken with III-V material processing and recently with Silicon photonics. Photonic integration offers a rich library of various components with reduced latency, higher bandwidth, and energy efficiency. It also facilitates nonlinear optoelectronic devices along with photonic/electronic integration and compact packaging.

The chapter is organized as follows: Section 2 covers background information on Neurons, and the efficiency of information processing in the human brain compared with other available technologies, it also covers addresses photonic tensor cores, the basic architecture of photonic neuron, and how the information is coded. Section 3 introduces photonic neuron based semiconductor lasers with

optoelectronic feedback, along with feedback control theory, and the equations covering self-pulsating mode of laser neuron system. Section 4, presents simulation results and noise analysis of the photonic neuron system, while Section 5, provides the concluding remarks of this work.

strongly correlated with different cognitive tasks, but they appear as a chaotic signal

The key driver for neuromorphic photonic approach especially in Machine-Learning, is the move from electronic processing approach in hardware systems such as Google's Tensor Processing Unit (TPU) which relies on grinding through stacks of repeated matrix multiplications that require immense amount of power. Neuromorphic photonic approach facilitates a photonic solution that is a modular photonic tensor core (PTC) where all matrix calculations get processed in the optical domain. This PTC process provides three orders of magnitude more computing power than TPU with processing of the multiply-accumulate matrix (MAC) operations. TPU data also exhibit long run times especially when performing image processing. While PTC can use the wave nature of light to, directly perform summation using coherent addition of wave amplitudes, and multiplications can result from the interaction of optical waves with matter. At the same time, and similar to the behavior of biological neurons, where each neuron can both process and store data, PTC replicates these functions, which dramatically reduces latency [5].

The neuron consists of an input stage that is a linear combination (weighted addition) of the outputs of the neurons feeding it. The combined signals from the input stage are integrated to produce a nonlinear response as dictated by the

This neuron must perform three mathematical operations:

All inputs must be of the same nature as outputs.

plot when plotted against time from physics and mathematics perspectives.

**2.2 Photonic tensor core**

*Neuromorphic Photonics*

*DOI: http://dx.doi.org/10.5772/intechopen.94297*

**2.3 Photonic neuron**

activation function see **Figure 2**.

3.Nonlinear transformation

2. Spatial summation

**Figure 2.**

**5**

*Neuron signal flow model [6].*

1.Vector (weighting) multiplication
