**5. Existing neuromorphic systems**

### **5.1 The Tianjic Chip**

This chip is the world's first hybrid-paradigm chip for brain-inspired computing to facilitate the development of artificial general intelligence (AGI) (**Figure 3**).

Two key approaches to the creation of AGI are computer science- and neuroscience-based. Both have benefits and drawbacks of their own. The combination of these two methods is currently thought to be one of the most promising approaches to *Neuromorphic Computing between Reality and Future Needs DOI: http://dx.doi.org/10.5772/intechopen.110097*

**Figure 3.** *Tianjic chip architecture [29].*

artificial general intelligence (AGI), and a strong hardware foundation that supports hybrid-paradigm computing is one of its key pillars.

The CBICR team suggests a novel brain-inspired computing architecture, a hybrid-paradigm Tianjic chip, which can simultaneously support computerscience-oriented and neuroscience-oriented neural networks and capitalise on their strengths, such as artificial neural networks and spiking neural networks. This architecture is based on the computing principles of brain science. The second-generation Tianjic chip was introduced in 2017 and was built upon the first-generation chip created in 2015. The modern Tianjic achieves versatile functionality, fast speed, and low power after continual design refinement. Tianjic has 20% higher density, 10 times the speed, 100 times the internal bandwidth, better flexibility/scalability, and more complete functionality than IBM's TrueNorth chip. The group has also created a first-generation software tool chain that facilitates compilation and model mapping automatically.

As a flexible and scalable AGI development platform, an unmanned bicycle was built. Using just one chip, it was able to perform a variety of complex tasks, including speech recognition, object tracking and detection, balance control, obstacle avoidance, and decision making. This device outperformed a comparable GPU 160 times faster and 120,000 times more effectively thanks to its 40,000 neurons and 10 million synapses. This book offers a fresh perspective and a new venue for academic study of AGI, which will facilitate its advancement and effects on business.

### **5.2 Intel's Loihi Chips and Intel's Pohoiki Beach computers**

Intel improved its Pohoiki Beach neuromorphic system, paving the way for quicker processing to be employed in AI for the Internet of Things and autonomous vehicles and equipment. More than 60 partners will use the system to solve challenging challenges that need extensive number crunching. It is built to imitate 8 million neurons and runs 64 Loihi research chips.

#### *Neuromorphic Computing*

Loihi, which was originally launched in 2017, has 10,000 times greater efficiency and can process information up to 1000 times faster than CPUs. Researchers will be able to scale up sparse coding, simultaneous localization and mapping (SLAM), and path planning to learn from new data inputs and adapt.

According to Applied Brain Research, the advantages of Loihi also include decreased power consumption, which is around 109 times lower than a GPU and five times lower than comparable IoT inference hardware.

Loihi was motivated by how the brain approaches problem-solving, which is the fundamental origin of all neuromorphic research. According to Intel, neuromorphic computing is computer technology that mimics the brain's neural structure and can use context-sensitive reasoning, common sense, and deal with ambiguity and contradiction. AI in its early stages was less robust, literal, and deterministic.

According to Prof. Konstantinos Michmizos of Rutgers University, "Loihi allows us to create a spiking neural network that imitates the underlying neuronal representations and activity of the brain." His lab was able to operate mobile robots precisely and with 100 times less energy while using a Loihi-run network as opposed to a commonly utilised CPU.

Neuromorphic computing-based AI has been touted by Intel as having applications in medical imaging and autonomous vehicles (AV). With AV, solutions could emerge to deal with uncertainties, like a ball rolling onto the roadway or an aggressive driver in another car on the road. AI can be used to highlight areas of a medical image where an ailment is more likely to be present.

Without providing a detailed roadmap, Intel stated that their research will eventually result in the commercialization of neuromorphic technology. Moore's Law and process-node computing cannot continue to expand, hence there is a critical need to sustain the gains in computing power and performance. According to Intel, specialised architectures like the Pohoiki Beach neuromorphic approach are required for particular developing applications like AV, smart homes, and cybersecurity (**Figure 4**).
