What is a Neural Network?

A neural network (also called an artificial neural network, ANN) is a computing model inspired by the human brain’s network of neurons, designed to process complex data by learning patterns rather than relying on explicitly coded rules. For developers, system‑architects and embedded‑AI engineers, neural networks provide a scalable framework to train multiple algorithms together and deploy inference across devices from cloud servers to tiny edge processors.

Why does a Neural Network Matter?

Neural networks underpin much of today’s AI revolution: they enable image‑recognition, natural‑language processing, even large language models (LLMs), by transforming raw data into learned representations. In the hardware and embedded‑systems domain, neural networks allow inference tasks to move closer to the sensor or endpoint, reducing latency, improving energy efficiency and enabling on‑device intelligence via specialized processors (such as NPUs and NPUs). Deploying efficient neural‑network inference on low‑power hardware is a key differentiator for edge‑AI applications.

How does a Neural Network Work (at a High Level)?

  • A neural network typically consists of three building blocks: an input layer (receives data), one or more hidden layers (process and transform data via weights and biases), and an output layer (generates predictions).
  • Each “neuron” computes a weighted sum of its inputs, applies a nonlinear activation function (such as ReLU or sigmoid), and passes its result to neurons in the next layer.
  • During training, the network uses back‑propagation and an optimization algorithm (e.g., gradient descent) to adjust weights so that the output error is minimized.
  • At inference time, the trained network uses the fixed weights and structure to map new inputs to outputs efficiently, often accelerated by hardware (GPUs, NPUs, dedicated ASICs).

Key Types of Neural Networks

Typical Use‑Cases in Hardware and Embedded Systems

  • On‑device voice assistant: A neural network runs on an NPU in a smart speaker to recognize wake words locally without cloud latency.
  • Smart IoT anomaly detection: Deployed in a sensor hub, a network identifies unusual patterns in vibration data to trigger predictive maintenance.
  • Mobile computer vision: Running a CNN on a mobile GPU to detect faces or gestures in real‑time.
  • LLM or generative AI acceleration: Deep neural networks running on specialized silicon to enable low‑latency inference for chatbots or image generation.

Related Considerations and Next‑Steps

  • Hardware acceleration: Efficient neural‑network inference demands specialized computation (e.g., MACs, quantization, sparsity) and hardware architectures tuned for it.
  • Quantization and optimization: To deploy on edge devices, neural networks must often be quantized (e.g., 8‑bit weights) and optimized for power/latency trade‑offs.
  • Domain alignment: The architecture, training strategy and deployment platform vary significantly across cloud, mobile and embedded.
  • Continual learning and update: Edge devices may need to support incremental updates or on‑device retraining for evolving data distributions.

Relevant Resources

Related Topics

  • Convolutional Neural Network (CNN): A neural‑network architecture that uses convolutional layers to detect spatial patterns, primarily used in image and video processing.
  • Recurrent Neural Network (RNN): A neural‑network architecture that maintains internal memory by looping over sequence data, suited for speech or time‑series analysis.