Hardware Neuron: Modeling Biological Neuron for Advanced Artificial Intelligence
May 03, 2023
Story
The advancement in Artificial Intelligence (AI) is largely attributed to the computational power of Artificial Neural Networks (ANNs). ANNs are designed to simulate the behavior of biological neurons, which are the fundamental building blocks of the brain. They have a wide range of applications, including image recognition, speech recognition, natural language processing, and many others.
To fully appreciate the potential of ANNs, it is important to understand the basic principles of neurons, their hardware structure, and their function in neural networks. By unlocking the full potential of neurons and optimizing their hardware structure, we can achieve even greater advancements in AI. These continuous discoveries can lead to faster speeds and reduced energy consumption, resulting in more efficient and effective AI systems.
Biological and Hardware Neurons
A fascinating aspect of neuroscience is the intricate structure and function of biological neurons, which have inspired the development of powerful neural hardware. Figure 1 illustrates the complexity of a biological neuron, with dendrites that receive inputs and the cell body performing intricate information processing.
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Fig.1. – A biological neuron |
This complex system was successfully developed using a simpler mathematical model called a perceptron, which paved the way for the development of neural hardware, as illustrated in Figure 2. The perceptron model is unlike the Izhikevich and Hodgkin-Huxley models, which are more complex and biologically realistic. The Izhikevich model captures the spiking behavior of neurons while the Hodgkin-Huxley model captures the biophysical properties of the neuron membrane.
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Fig.2.- A hardware neuron |
Use of Multiplier Accumulator Unit (MAC)
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Fig.3. – MAC schematic |
In neural hardware, a Multiplier Accumulator Unit (MAC), as illustrated in figure 3, is used to emulate the functions of dendrites and the cell body of a biological neuron that receives and processes input signals. The output of the MAC is then transmitted through an activation, which generates an electrical signal when the total strength of the input signals surpasses a predetermined threshold. This process mimics the function of a biological axon synapse, which transmits an output electrical signal when the strength of the input signals reaches a certain threshold.
The Perceptron is a remarkable mathematical model which is computed by the addition of the product of each weight input value. In this way, the perceptron can mirror the behavior of the biological neuron and produce outputs based on the strength of its inputs. This phenomenon is observed in hardware neurons as illustrated in Figure 3. The MAC performs a series of computations like those of a perceptron, where the total strength of input signals is calculated by summing up the values of the weighted inputs.
Synthesis and Simulation of Neuron
The simulation results of a neuron synthesized in FPGA for a 2-input single-bit equivalent to logical OR operation is presented in Figure 4. The waveform displays the output of the neuron for various input combinations of 0s and 1s. As expected, the output is 1 when either input is 1, and 0 when both inputs are 0, which depict application in input table 1.
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Input |
Input |
Output |
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Sensor 1 |
Sensor 2 |
Light |
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OFF |
OFF |
OFF |
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OFF |
ON |
ON |
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ON |
OFF |
ON |
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ON |
ON |
ON |
Input table 1.
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Fig.4. – Simulated waveform result of a hardware neuron |
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Fig.5. – Synthesized schematic of the neuron |
This demonstrates the ability of hardware neurons shown in Figure 5 to perform logical operations and serve as building blocks for more complex neural networks. Implementing these networks on FPGA or ASIC offers the benefits of high speed and low power consumption, making them well-suited for a wide range of real-time applications.
Hardware Neural Network Applications
The significance of neural networks within the field of artificial intelligence is far-reaching and is an essential component for many cutting-edge technologies and advancements. Hardware neurons are used for the creation of AI systems that can learn and adapt to new and unpredictable situations and perform on-the-fly decision-making for enhanced human safety, comfort, and operational efficiency.
Figure 6 shows an intelligent temperature control system that utilizes AI to monitor outdoor temperature and adjust indoor temperatures based on occupants’ comfort settings.
An AI-based lighting system can optimize and tune light parameters accordingly to enhance user experience and wellbeing
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Fig. 6 Intelligent self-adjusting temperature control |
Figure 7 shows the significance of real-time applications where AI observes and measures the indoor environment and activates lights and mirror displays as soon as an individual is detected within the perimeter.
To meet such requirements, the integration of AI custom neuron hardware can play a vital role by providing higher energy efficiency and instantaneous response to the sensor signals.
Another key advantage of using custom hardware is low-latency operation. An AI custom ASIC inference hardware neural network can efficiently process and analyze data in real time with faster speed and greater accuracy without the need for constant communication with the cloud. As AI technology continues to evolve, the demand for inference hardware neural networks will increase since they offer a powerful, efficient solution for real-time AI applications.
The development of custom AI hardware neurons is a critical step in the evolution of advanced AI, and it holds great promise for the creation of intelligent systems that can improve our world in countless ways.
Shreyas Basavaraju is an FPGA Design Engineer at Socionext America. He holds a Master's Degree in Electrical Engineering and has over a decade of experience in FPGA prototyping and Python automation. Having a strong passion for technology and innovation, Shreyas has prototyped various SOCs on FPGAs throughout his career, developed automation flows, and utilized his programming skills to provide solutions to diverse industries, including consumer electronics and wireless telecommunications and Io
