A neuromorphic chip is an analog data processor inspired by the biological brain. The word neuromorphic itself derives from the words neuro, which means "relating to nerves or the nervous system," and morphic, which means "having the shape, form or structure." This concept of design allows these chips to interpret sensory data and respond in ways that are not specifically programmed. Stemming from the field of neuromorphic engineering, researchers apply biology and physics to mathematics, computer science and electronic engineering to create technology that mimics an artificial neural network (ANN). The concept of a neuromorphic computing chip was coined by Carver Mead in the 1980’s and has sparked research and interest from universities and companies such as Intel, IBM, and Qualcomm.
Presently, computers utilize the von Neumann architecture for processing data. This method sends information back and forth between the central processor and memory chips, following a very linear pattern. In contrast to this, neuromorphic chips encode and shuttle data in a series of electrical bursts, modeled after the synapses of the brain. Synapses in biological brains respond to sensory stimuli and change their connections based on learned experience. Neuromorphic chips will develop in the same way by etching its neural networks into silicon and be able to combat issues never before faced by the processor due to its past encounters.
In comparison to general-purpose chips, neuromorphic chips are not as flexible or powerful. However, they can be specialized to specific tasks more efficiently than other solutions and are, therefore, more energy efficient. Larger amounts of information can be packed into each “burst” from the chip, making it able to encode and understand more data in less time.
The continuing advancement of neuromorphic chips sparks important implications for technology and its developers. The most obvious is that artificial intelligence and the machines utilizing this technology will be able to interact with the world in a more human-like fashion. It also introduces features to technology such as the ability to recover from damage, adapt to change, and develop based on learned principles. With a lower energy demand, neuromorphic chips would also allow developers to incorporate AI abilities into a wider range of devices.
Potential Uses for Neuromorphic Chips:
- Glasses utilized by the blind that use sensors to identify objects and its surroundings, returning audio cues to the user
- Medical devices that track a patient’s records over time, thus identifying warning signs of a problem or adjusting treatments as necessary
- Smartphones that make suggestions based on surroundings or interrelated applications
- For example, automatically entering do not disturb mode when the user goes to bed, providing background information on a client before a meeting, or taking pictures of recognized individuals in a scene