The term ‘neuromorphic computing’ can be traced back to the 1980s when Caltech researcher Carver Mead first proposed the concept of designing integrated circuits (ICs) to mimic the organization of living neuron cells. As the National Institute of Standards (NIST) states, neuromorphic computing promises to dramatically improve the efficiency of computational tasks such as perception and decision making.
Nanoscale oscillators and reconfigurable Josephson junctions
Although software and specialized hardware implementations of neural networks have made significant progress in recent years, such implementations are still are still ‘many orders of magnitude’ less energy efficient than the human brain. This is precisely why NIST researchers are working on several bio-inspired hardware implementations of neuromorphic networks. More specifically, one project is based on high-frequency room-temperature nanoscale oscillators utilizing the spin-torque effect, while the other is designed around dynamically reconfigurable magnetic Josephson junctions operating at liquid-helium temperatures.
Neuromorphic silicon: Loihi and Akida
On the silicon side, Intel introduced a self-learning neuromorphic chip dubbed ‘Loihi.’ The chip mimics
how the human brain functions by learning to operate based on various modes of feedback from its environment. The energy-efficient chip – which uses data to learn and make inferences – becomes smarter over time, without the need for traditional training methodologies. Rather, Loihi takes a novel approach to computing via asynchronous spiking.
Similarly, BrainChip developed the Akida NSoC, which the company describes as a neuromorphic chip engineered on a digital logic process. Inspired by the biological function of neurons, these spiking neural networks (SNNs) are inherently lower power than traditional convolutional neural networks (CNNs). Indeed, SNNs replace the math-intensive convolutions and back-propagation training methods of CNNs with neuron functions and feed-forward training methodologies.
IBM’s True North neuromorphic chip
A patent filed by IBM in late 2018 offers a rare insight into the company’s True North neuromorphic chip. According to Nicole Hemsoth of The Next Platform, one of the elements making IBM’s neuromorphic device more efficient and scalable is a new algorithm that determines placement, or how a neurosynaptic core in True North is mapped onto the chip (and even across chips). In addition, the patent details how the new device supports standard deep learning frameworks that can be used and converted during offline training. As Hemsoth explains, the trained networks are converted into corelets after training and subsequently converted into model files that can be loaded onto specific neurosynaptic substrates.
Nengo and algorithms
On the software side, Applied Brain Research developed Nengo, an advanced tool that allows programmers to more easily run AI algorithms on new cortical chips. According to Wired, the Python-based compiler is targeted at AI applications that will run on general purpose neuromorphic hardware. Nengo has already been used to develop vision systems, speech systems, motion control and adaptive robotic controllers.
Memory for neuromorphic systems
As a recent article in Advanced Material Technologies emphasizes, artificial neurons should closely mimic biological neurons, while artificial synapses should accurately emulate biological synapses. Both artificial implementations must also be power‐efficient, scalable and capable of implementing relevant learning rules to facilitate large‐scale neuromorphic functions. Thus far, a wide range of memory devices have been utilized to realize the goal of creating efficient artificial synapses. These include resistive random‐access memory (ReRAM), diffusive memristors, phase change memory (PCM), ferroelectric field‐effect transistors (FeFET), spintronics-based MRAM and synaptic transistors.
“Each of these devices has its own strengths and weaknesses. One type of device can be preferred over the others depending on the requirements for a specific application,” the article states. “Although attempts have been made to implement logic circuits using emerging devices, however, at least in the near future, neuromorphic systems still need the mature and reliable CMOS circuitry to implement peripheral components, which means neuromorphic systems and CMOS circuits will complement rather than replace each other.”
Interested in learning more about various types of memory? You can browse our article archive on the subject here.
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