There are three main ingredients to creating artificial intelligence: hardware (compute and memory), software (or algorithms), and data. We've heard a lot of late about deep learning algorithms that are achieving superhuman level performance in various tasks, but what if we changed the hardware? Firstly, we can optimise CPU's which are based on the von Neumann architectures that we have been using since the invention of the computer in the 1940's. These include memory improvements, more processors on a chip (a GPU of the type found in a cell phone, might have almost 200 cores), FPGA's and ASIC's. Such is the case with research being done at MIT and Stanford.
Von Neumann Architecture Neuromorphic Architecture Neuromorphic architectures address challenges like high power consumption, low speed, and other efficiency-related bottlenecks prevalent in the traditional von Neumann architecture Architecture Bottleneck CPU Memory Neuromorphic architectures integrate processing and storage, getting rid of the bus bottleneck connecting the CPU and memory Encoding Scheme and Signals Unlike the von Neumann architecture with sudden highs and lows in the form of binary encoding, neuromorphic chips offer a continuous analog transition in the form of spiking signals Devices and Components CPU, memory, logic gates, etc. Artificial neurons and synapses Neuromorphic devices and components are more complex than logic gates Versus Versus Versus 10. NEUROMORPHIC CHIPSETS 10 SAMPLE REPORT Neuromorphic Chipsets vs. GPUs Parameters Neuromorphic Chips GPU Chips Basic Operation Based on the emulation of the biological nature of neurons onto a chip Use parallel processing to perform mathematical operations Parallelism Inherent parallelism enabled by neurons and synapses Require the development of architectures for parallel processing to handle multiple tasks simultaneously Data Processing High High Power Low Power-intensive Accuracy Low High Industry Adoption Still in the experimental stage More accessible Software New tools and methodologies need to be developed for programming neuromorphic hardware Easier to program than neuromorphic silicons Memory Integrated memory and neural processing Use of an external memory Limitations • Not suitable for precise calculations and programming- related challenges • Creation of neuromorphic devices is difficult due to the complexity of interconnections • Thread limited • Suboptimal for massively parallel structures Neuromorphic chipsets are at an early stage of development, and would take approximately 20 years to be at the same level as GPUs. The asynchronous operation of neuromorphic chips makes them more efficient than other processing units.
Both projects are part of the European Human Brain Project, originally funded by the European Commission's Future Emerging Technologies program (2005-2015). With more than one million cores, and one thousand simulated neurons per core, SpinNNaker should be capable of simulating one billion neurons in real-time. Dharmendra Modha, IBM fellow and chief scientist for brain-inspired computing, wrote an interesting commentary on the TrueNorth project that traces development of von Neumann architecture based computing and contrasts it with neuromorphic computing approaches: Introducing a Brain-inspired Computer. TrueNorth chip, introduced in August 2014, is a neuromorphic CMOS chip that consists of 4,096 hardware cores, each one simulating 256 programmable silicon "neurons" for a total of just over a million neurons.
Neuromorphic computing or neuromorphic engineering has been described as the use of large integration systems containing numerous analog circuits allowing the replication of neuro-biological behaviors existing in a human's nervous system. The neuromorphic computing market platform consists of two vital systems based on the custom hardware architecture. Such systems are designed to program neural microcircuits by applying brain-like thought process in cognitive computing and machine learning process. This procedure enables a machine to learn, adapt and function like a human brain does rather than functioning like a normal computer. In addition, to perform such a complex task, the computing platform requires the state-of-the-art circuit technologies and electronic components, which allows the platform to receive new data or knowledge gained from various other sources of neuroscience research, e.g.
Neuromorphic engineering, also known as neuromorphic computing, is a concept developed by Carver Mead, in the late 1980s, describing the use of very-large-scale integration (VLSI) systems containing electronic analog circuits to mimic neuro-biological architectures present in the nervous system. In recent times, the term neuromorphic has been used to describe analog, digital, mixed-mode analog/digital VLSI, and software systems that implement models of neural systems (for perception, motor control, or multisensory integration). The implementation of neuromorphic computing on the hardware level can be realized by oxide-based memristors, spintronic memories, threshold switches, and transistors. A key aspect of neuromorphic engineering is understanding how the morphology of individual neurons, circuits, applications, and overall architectures creates desirable computations, affects how information is represented, influences robustness to damage, incorporates learning and development, adapts to local change (plasticity), and facilitates evolutionary change. Neuromorphic engineering is an interdisciplinary subject that takes inspiration from biology, physics, mathematics, computer science, and electronic engineering to design artificial neural systems, such as vision systems, head-eye systems, auditory processors, and autonomous robots, whose physical architecture and design principles are based on those of biological nervous systems.