ARTIFICIAL BRAIN
Technology That Makes Machines Smarter
Artificial Brain is a neuromorphic model that exactly imitates the information processing method of the human brain.
The term is derived from the words 'neuro' (pertaining to the nervous system or brain) and 'morphic' (pertaining to shape or structure) and means brain-like. From a broader perspective; Neuromorphic systems are brain-like systems that aim to perform complex calculations more quickly and energy efficiently by mimicking the brain's information processing strategies. This innovative technology brings a new perspective to the evolution of computer science and artificial intelligence by taking a different approach from traditional computer systems.
In this article, we will discuss the basic principles of neuromorphic systems, their architecture, how they work, and how they differ from traditional computer systems. Additionally, while focusing on the challenges and obstacles faced by this new technology, its potential application areas and its possible effects on the future of artificial intelligence, we will touch upon how the Artificial Brain model eliminates these challenges and obstacles.
Basic Principles of Artificial Brain
Memory Power
Since the Artificial Brain model is a neuromorphic system, it differs from traditional computers in terms of the methods of processing and storing information. In traditional computers, the processor and memory are separate, which requires constant data transfer between processing and storing information. On the other hand, in neuromorphic systems, information processing and memory are combined, as in the brain. This reflects how complex operations can be performed quickly and energy efficiently by the Artificial Brain model.
Instant Prediction
Another important principle of the Artificial Brain model is to mimic the parallel processing ability of the brain. The human brain provides simultaneous communication of information between millions of neurons. The Artificial Brain mimics this parallel processing ability, allowing multiple calculations to be performed simultaneously. In this case, with its memory power, it can easily predict what will happen next with each signal input.
Adaptive Learning
Additionally, the Artificial Brain is based on artificial neural networks and therefore has learning and adaptation capabilities. Like brain plasticity, the Artificial Brain can update its connections with new information and thus develop solutions to previously encountered problems, while also modeling new cases on its own if it has already experienced some of the situations included in the new cases.
Basic Principles of Neuromorfic Computing
Neuromorphic computing is a computer science approach that mimics the processing and learning mechanisms of the human brain. This approach is based on the following basic principles:
Nerve Cells and Synapses
Nerve cells (neurons) and synapses in the brain are the basic building blocks of neuromorphic computing. Neurons are cells that process and transmit information; Synapses provide information transmission between neurons and represent learning and memory processes.
Parallel Processing
The human brain can perform many operations simultaneously, showing a complex parallel processing ability. Neuromorphic computing mimics this parallel processing feature, offering the ability to perform large numbers of computational operations on the fly.
Adaptive Learning
The brain has the ability to adapt by learning from experiences and interactions. Neuromorphic computing attempts to mimic this adaptive learning ability by building on the concepts of neural networks and neuroplasticity.
Energy Efficiency
The human brain performs complex calculations at low energy levels. Neuromorphic computing systems include efforts to mimic this energy efficiency.
SNNs
SNNs stands for “spiking neural networks.” Neuromorphic computing has been developed based on Spiking Neural Networks (SNNs), which model the interactions of neurons. SNNs can more precisely mimic the real-time activities and dynamics of neurons.
Plasticity
The brain can reshape itself depending on experiences thanks to neuroplasticity. Neuromorphic computing systems attempt to mimic this property of plasticity by simulating changes in weight and connections. For example, when an event that has been experienced before is repeated, the connections between neurons become stronger; if an event that has been experienced once and nothing similar to this event is experienced again, the connections between neurons become weaker, come to the point of disappearing over time, and "forgetting" occurs.
These fundamental principles come together to advance a broader understanding of the field of neuromorphic computing and lay the foundation for new research and applications. Each helps us better understand the brain's complex dynamics and processing capacity.
Comparison of Neuromorphic Systems and Traditional Computers
Traditional Computers
Neuromorphic Systems
Benefit
Structure
and
Design
Von Neumann architecture;
The processor and memory are separate, data must be constantly moved between the two. This leads to a performance limitation called the Von Neumann barrier.
Neural structure;
Information processing and memory modeling are a single process. This consumes less energy because it does not involve data migration.
Energy efficiency is at maximum level in neuromorphic systems.
Proccessing Information
Sequential processing;
The processor performs a single operation at a given time.
Parallel processing;
It performs many operations simultaneously. This allows it to perform complex calculations faster.
Computational power and speed are on average 100-1000 times higher than neuromorphic systems.
Learning
It follows pre-programmed commands. There is no self-learning.
Since it models all senses and perceptions in the same way on the nervous system, it learns on its own and adapts to new situations it encounters.
Since there is adaptive learning in neuromorphic systems, data science and offline training are minimized. Costs are reduced.
These differences show that neuromorphic systems have significant advantages over traditional computers in applications such as artificial intelligence, machine learning and data analysis.
The Artificial Brain model is based on these basic principles of neuromorphic systems; Since it is a self-learning model in real time, it minimizes the data science process, offline training process and model development costs.
Neuromorphic Architectures and Hardware
Today, many companies, universities and research groups have developed and made available neuromorphic systems. In this field, accelerators, semiconductors and chips based on neuromorphic computing have taken their place in the market. Here are the architectures and hardware that emerged as a result of these researches;
DYNAP-SE2
Institute of Neuroinformatics
DYNAP-SE2 is a configurable, mixed-signal neuromorphic chip featuring 1024 neurons, 64k plastic synapses, specialized dendrites, low-latency event routing, and multi-timescale adaptation dynamics, enabling real-time prototyping of bioinspired neural networks for ultra-low-level signals.
TrueNorth
IBM
IBM's TrueNorth architecture is a neuromorphic chip design that simulates one million programmable neurons and 256 million programmable synapses. This design attempts to mimic the complex processing capabilities and energy efficiency of the brain.
Dynap-CNN
SynSense
DynapCNN is an ultra-low-power, event-driven neuromorphic processor chip for accelerating neural networks that perform sub-milliwatt computations using in-memory techniques. With 1 million neurons, it can implement convolutional network models such as LeNet and ResNet and directly interface to sensors such as DVS cameras for low-latency, always-on view applications.
Speck
SynSense
A DynapCNN chip paired with an event-based camera on the same die.
SpiNNaker 2
University of Dresden
The SpiNNaker 2 chip packs 144 ARM cores with 18MB on-chip SRAM, 8GB DRAM, and custom math accelerators. Manufactured at 22nm, this device delivers a 50x increase in neural simulation capacity per watt over SpiNNaker 1, using body bias and DVFS for near-adaptive threshold operation down to 0.4V.
Loihi 2
Intel
Loihi 2 is Intel's latest neuromorphic research chip and implements incremental neural networks with programmable dynamics, modular connectivity, and optimizations for scale, speed, and efficiency. Early research shows promise for low-latency intelligent signal processing.
Xylo
SynSense
Xylo is a 28nm 1000-neuron digital spiking neural network inference chip optimized for ultra-low-power edge deployment of trained SNNs, with a flexible architecture for mapping various network topologies.
BrainScaleS 2
Universität Heidelberg
BrainScaleS-2 is an accelerated neuromorphic system-on-chip integrating 512 adaptive integrating and firing neurons, 212k plastic synapses, embedded processors, and event routing. It enables rapid imitation of complex neural dynamics and discovery of the rules of synaptic plasticity. The architecture supports training deep spiking and non-spiking neural networks using hybrid techniques such as surrogate gradients.
Spiking Neural Processor T1
Innatera
Spiking Neural Processor T1 is Innatera's ultra-low-power neuromorphic microcontroller SoC for near-sensor real-time intelligence. It integrates a spiking neural network accelerator, a convolutional neural network accelerator, and a RISCV core. T1 targets applications on battery-powered, power-limited, and latency-critical devices.
Artificial Brain Model and Neuromorphic Chips
Since the Artificial Brain model has the power to perform parallel information processing like the human brain, unlike the sequential processing architecture of the CPU and GPU, the hardware on which it works best and most accurately is neuromorphic chips.
Neuromorphic chips currently produced and used today can be programmed with software produced by manufacturers to program the relevant chip. However, in order to program the neuromorphic chip by performing neuromorphic calculations, the R&D process and long-term programming costs arise. Moreover, different manufacturers have different software tools for their own chips and are only useful for programming the chips they produce.
Manufacturer
Chip
Chip Programming Tool
Intel
Loihi 2
Lava
BrainChip
Akida
MetaTF
IBM
TrueNorth
Neuro AI Toolkit
General Vision
NM500
NeuroMem
NVidia
Orin
Jetson
While the chip programming tools provided by neuromorphic chip manufacturers in the market only serve to program their own chips, we offer the MindCraft tool, which allows the Artificial Brain model to be implemented on any neuromorphic chip.
MindCraft
Plug-and-Play Brain Generation Tool
MindCraft is a software that enables the implementation of the Artificial Brain Model on neuromorphic chips without writing code. Through MindCraft , you can download a blank Artificial Brain model onto the chip and complete field training on the device. Or you can test your device in a simulation environment in MindCraft and have the Artificial Brain learn on its own and model the memory during this simulation. After downloading the resulting memory to the chip with one click, you will have a plug-and-play brain in your hand!
Using MindCraft 's Artificial Brain model; Apart from programming the chip without writing code, another unique feature is “memory transfer”. Imagine a self-propelled drone; It learns itself in real time and has a much faster reaction time - maneuverability - faster completion of tasks and higher battery life than drones with CPU-GPU. By taking the memory of this used drone from the chip via MindCraft ; You can download it to all drones in your fleet with just one click. This way, you don't have to bear the cost of retraining or model development.
Chip Programming Tool
Supported Chips
No-Code
Deployment
Memory Transfer
MindCraft
CONFEDERATION AI
All
+
One-Click
+
Lava
INTEL
Loihi 2
X
SDK
X
MetaTF
BRAINCHIP
Akida
X
SDK
X
Neuro AI Toolkit
IBM
TrueNorth
X
SDK
X
NeuroMem
GENERAL VISION
NM500
X
SDK
X
Jetson
NVIDIA
Orin
X
SDK
X
Benefits Provided by Neuromorphic Chips Modeled with Artificial Brain
Although neuromorphic chips have advantages over CPU and GPU in terms of energy consumption, calculation speed and latency, they must be modeled in accordance with their nature in order to benefit from these advantages at the maximum level. Neuromorphic chips implemented in the Artificial Brain model directly translate these advantages into the following benefits. In real time;
Adaptive Learning
Devices become smarter and more functional by constantly learning from experience and data without being programmed.
Singular Models of Complex Signal
Devices create memory by associating complex signals such as sound, image and vibration with each other.
High Computing
Power
Neuromorphic chips can perform more complex operations while consuming less power compared to traditional processors.
Less Energy Consumption
Devices operate with less energy, providing longer battery life and more sustainable use.
Low Latency
Since transactions occur in real time, latency is minimized, providing instant response and control.
Data Privacy and Security
Since data is processed on the device, the need for edge computing, data transfer and cloud storage is eliminated, providing a high level of security.
24/7 Accessibility
Devices can operate continuously without the need for an internet connection and are ready for use at any time.
In summary: The Artificial Brain model expands the boundaries of artificial intelligence to the human brain by enabling devices to become smarter, faster, more efficient and safer with neuromorphic chips.
Application Areas of Artificial Brain Model
Thanks to the Artificial Brain's energy-efficient and real-time adaptive learning capabilities, it models sound and vision in the same way. This eliminates the need to develop a new artificial intelligence model for each different task in different sectors, and maximizes signal processing on any device with a sensor.
Turn plug-and-play BoC DEAE's ability to extend field life with energy efficiency, self-learning the field with its adaptation ability, and fast decision-making capabilities with low latency, to your advantage in the field and gain superiority.
Get ahead of the curve with the BoC Autonmy neuromorphic chip, accelerating your autonomy and electrification roadmap for complex L4/L5 autonomous cars. We provide an autonomous, environmentally friendly driving experience that saves lives..
We see an all autonomous future. And not just for cars.
Rapidly deploy intelligent systems, accelerate your automation pipeline, increase efficiency and safety, and extend your operating time – all while lowering your TCO.
Build tomorrow’s neuromorphic robotics systems, today.
BoC Health delivers the most powerful on-device processing of medical data with low power utilization, blazing-fast predictions, and multi-purpose backends. Easy-to-use neuromorphic chip allows you to focus on what you are best at without worrying about privacy, performance and efficiency.
Neuromorphic chip extends the flight time of drones by 2 times with its energy efficiency, enables it to respond 20 times better thanks to its high processing power, and lightens the chip hardware required for flight.
Your cameras now focus faster with BoC Vision low latency, detect changes with 99.99% accuracy with self-learning, and immediately recognize and interpret new assets in real time.
These application areas show that Artificial Brain can be used in a wide range. However, technological developments and scientific understanding in this field are still advancing rapidly, so the potential applications of the Artificial Brain will not be limited to these.