**2.2 Photonic tensor core**

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

In this section, we survey competing technologies and their architectures while

The human brain contains around 100 billion neurons. It is the most complex system for information processing in existence, with an execution power of 10e18 (multiply-accumulate matrix) MAC/s with only 20 Watts power needed [2, 3]. Each of those neurons has an average of 10e4 inputs of tiny neurotransmitter junctions known as synapse. This translates into 10e15 of synapses connections, while the bandwidth of the signal processed in the brain is 1 KHz max. The computational efficiency of the brain is less than 1 AttoJoules/MAC while supercomputers today have an efficiency of 100 picoJoules/MAC, in other word, the magnitude of the brain efficiency is 8 orders better than supercomputers as can be seen in **Figure 1**. The neuron by definition is a single brain cell. It communicates with other neurons with electrical impulses via thousands of synapses. The neurotransmitters (chemical secretions) electrical input to the neuron comes from other neurons with different weights. The signal level needs to exceed a certain threshold value to cause the neuron to fire, sending a series of electrical spikes to other neurons. Below threshold input, the neuron output is very small and linear, while above threshold, the neuron output is large and nonlinear. This behavior is similar to how lasers operate; below threshold, the optical output is made of spontaneous emission, low output incoherent optical power, while above threshold, stimulated emission dominates and produce large coherent optical light output. A key requirement to have the laser behave as a neuron is to include an absorber section to limit the spontaneous emission output below threshold levels. These electrical activities in the brain can be monitored using ElectroEncephaloGraph (EEG), these signals can be

comparing them to the ultimate information processor in the human brain.

*Comparing computational speed with efficiency for various elements of available technologies [4].*

the concluding remarks of this work.

**2. Technology survey**

**2.1 Neurons**

*Optoelectronics*

**Figure 1.**

**4**

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].
