As the realms of technology and biology converge, hardware neurons powered by FPGA and ASIC are emerging as groundbreaking tools to unlock the next level of artificial intelligence. These bio-inspired components, a mainstay of robust artificial neural networks, have the potential to supercharge AI systems, offering enhanced speed, reduced energy consumption, and leading to more effective AI applications. In this post, we’ll look at the intricate world of hardware neurons synthesized in custom FPGA or ASIC and explore how this approach will revolutionize the AI landscape.
The computational power of artificial neural networks (ANNs) is the unsung hero driving the rapid advancement in artificial intelligence (AI). The design of ANNs simulates the behavior of biological neurons, which are the fundamental building blocks of the brain. ANNs have many applications, including image and speech recognition and natural language processing.
Fully appreciating the potential of ANNs requires us 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 with custom ASIC or FPGA implementations, 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.
Biology Inspires Hardware Neurons
Our journey to understanding AI’s power begins by delving into the fascinating world of neuroscience. The intricate structure and function of biological neurons, the brain’s essential building blocks, have inspired the development of advanced neural hardware. Figure 1 illustrates the complexity of a biological neuron, with dendrites receiving inputs and the cell body performing intricate information processing.
Researchers recreated this complex biological system into a simpler mathematical model called a perceptron, clearing the path for developing neural hardware (Figure 2). Unlike the more complex and biologically realistic Izhikevich (which captures the spiking behavior of neurons) and Hodgkin-Huxley (which capture the biophysical properties of the neuron membrane) models, the perceptron captures the fundamental behavior of neurons without all the biological details.
The Hardware Brain: Multiplier Accumulator Unit (MAC)
At the heart of neural hardware lies the multiplier accumulator unit (MAC) (Figure 3). This unit emulates the functions of a neuron’s dendrites and cell body, receiving and processing input signals.
Much like a biological axon synapse, the MAC transmits an output electrical signal when the cumulative strength of the input signals surpasses a predefined threshold. The MAC is a computational powerhouse, using the remarkable perceptron mathematical model to mirror a neuron’s input response by summing the weighted input signals to produce the final output.
Simulating Neurons for Real-World Solutions
Take a look at Figure 4 to see the simulation results of a neuron synthesized in FPGA and performing a logical OR operation with two single-bit inputs. The waveform showcases how the neuron reacts to combinations of 0s and 1s for its inputs. Consistent with our expectations, the output peaks at 1 when at least one input is 1 and drops to 0 when both inputs are 0, as visualized in input table 1.
The synthesized neuron, shown in Figure 5, demonstrates the capability of hardware neurons to execute logical operations, establishing them as crucial elements for more sophisticated 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.
The Future is Here: 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. Researchers and vendors are innovating with hardware neurons to create 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.
The range of AI applications using neural networks is far-reaching and exciting. The possibilities are endless, from intelligent temperature control systems that monitor outdoor temperatures and adjust indoor settings for optimal comfort (Figure 6) to AI-based lighting systems that observe and measure the indoor environment to activate lights and mirror displays as soon as an individual is detected within the perimeter (Figure 7).
Custom neuron hardware will play a vital role in meeting the demanding requirements of AI applications like the above by providing higher energy efficiency and instantaneous response to the sensor signals.
A 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 faster and more accurately without constant communication with the cloud. The demand for inference hardware neural networks will increase as AI technology evolves 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.
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Socionext offers the right combination of IPs, design expertise, and support to implement large-scale, fully customizable SoC and FPGA solutions to meet the most demanding and rigorous AI application requirements.
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