Spiki thrives for enabling Spiking Neural Networks.
Why? Because they are fast. They save energy. They save money.
They work like a human brain.
Commercially they are not yet available, which does not prevent us from researching and trying them.
The development of artificial intelligence (AI) is progressing rapidly. The next generation of AI is already on the rise. With this generation, we are bringing neural networks closer to human thinking and learning behavior. Because we are using a well-known concept from neuroscience here – neurons and the function of spikes.
A neural network is a way of machine learning mimicking the interconnected neurons of the human brain.
third generation AI is on the rise
The first generation of artificial intelligence was a rule-based imitation logic to make reasoned conclusions within a specific and limited problem. This type of application is ideal for monitoring or optimizing a process, like this: information input –> process output.
The second AI generation is called a deep learning network (DLN). DLNs can process large amounts of structured or unstructured data and can identify patterns in video, image, text or sound data. Real life applications are financial predictions, disease mapping, user / customer behavior analysis etc. With their interpretative capacity, DLNs approximate the computation efficiency of the human brain. A further reduction and miniaturization of their energy consumption for edge applications is underway.
The upcoming third generation of AI, which we describe below, takes a further leap from literal evaluations of input data to quasi understanding. This AI takes information just as a human does, as a sequence of events and not just a snapshot of reality like DLNs. This advancement of perception is based on neural network training and inference.
SNN: energy efficient learning process close to human thinking
As stated before, one of the major challenges in neuromorphic research is to mimic human flexibility. The ability to learn from unstructured stimuli while being as energy efficient as the human brain is a great challenge. The computer building blocks within neuromorphic computer systems are analogous to the logic of human neurons. A so-called Spiking Neural Network (SNN) is a new model for arranging these elements to physically reproduce the temporal component of the functionality of neurons and synapses. This enables greater energy efficiency and plasticity.
What to expect from SNNs? Challenges and perspectives
Currently, neuromorphic computing systems are still under investigation. So far, prototypes are being developed. The technology is quickly gaining momentum and large corporations such as Intel and IBM are involved in research projects.
Nevertheless, there are still some critical points that need to be considered so that the application of neuromorphic systems can be successful. On the hardware side, we are faced with limited memory bandwidth, which is the transmission speed between memory and processor. “In-memory-computing” can avoid this kind of bottleneck. As of yet, SNNs have been considered too difficult to train for commercial applications.
With Spiki’s training method we aim to enable SNN use in specific application areas and further include robustness and formal verification. With formal verification we aim to formally guarantee important properties of neural networks such as robustness, safety, and correctness. All of these are fundamental to make SNNs fit for real life and even fit for safety-critical applications.
On the software side, we must formulate use cases and define relevant problems in which neuromorphic systems can be used to generate enough training data. The problems solved by AI technologies so far are specifically formulated for use cases that don’t use neuromorphic computing. The neuromorphic approach gives us a different, promising perspective. Compared to other high-performance computing resources, neuromorphic systems offer faster speeds while consuming less power.
Surge forward, with Spiki’s robust AI tailored to your needs
Spiki wants to surge forward and join forces with companies in robotics, telecommunication, industrial automation, sensor engineering or hardware development to reap the benefits of SNNs for innovative use cases. We offer an innovative training framework and IP for supervised learning with audio, video or continuous sensor data. Our trained networks can be made robust against low-quality input data, such as white noise, slurred speech or imperfect material surfaces. We are striving to build trustworthy AI, which can be deployed via microcontroller, FPGA, as a cloud service or even ASIC as a future step in our product development. You profit from an easy to use toolchain, from training to deployment, ready for third-party hardware.
Voice assistants make our lives so much easier: simply activating our lights, dropping a phone call or playing our favourite music by just using speech commands, hands free. Several devices, assistants and routers provide us with formidable convenience at our spoken request. Of course, an active Internet connection is required for that. Sometimes different web apps come into play to further finetune and navigate tasks. It’s all quite simple and intuitive. Nothing to worry about.
Really?
Do you value, beside the mere convenience of using voice assistants, your data privacy and security? Where does your voice assistant keep your data? Does it listen to you ALL THE TIME, storing, processing or even sharing the most personal details?
Risks of online voice processing devices
Indeed, every user shares a great deal of information, even when only talking privately at home. Usually, these recorded data are stored and processed in a cloud. This poses a risk to security, safety and privacy.
Firstly, hackers and cybercriminals can gain access to data stored in a cloud system. Sensitive information could get into the wrong hands, e.g. access codes, passwords, financial or health information etc.
Secondly, a user’s voice is regarded as biometrical data which can identify a human being. Voice recordings stored locally are not in danger, but stored in a cloud they could get subject to abuse. Big tech corporations frequently store data generated from voice recognition devices in a cloud, analyse and use them in order to improve their software or their advertising.
Here is a prime example: “Amazon.com Inc. must produce millions of documents in response to discovery requests in a potential class action over the marketing of its Alexa-enabled devices and their recording of users’ conversations, a federal judge ruled”, reports Christopher Brown on Bloomberg Law (see article).
Improper or not – some big tech companies already offer their users to opt in and allow their voice recordings to be stored, while others require them to actively opt out if they do not want this. Feel free to check for yourself the terms of use of your trusted voice assistant to stay in control.
Any company’s information is a top security priority. In the wake of the COVID pandemic, many corporate meetings had to be transferred from personal to the online world. Video conference tools mainly use end-to-end encryption, which secures privacy and security of the online communication. Still, the encrypted video and audio content is stored in the cloud…you see where this is going, check first bullet point.
In summary, data privacy and cyber security are major challenges for both private and corporate users of voice processing software and devices using speech commands.
How to guard your privacy and security
There is only one reliable solution in order to protect your sensitive information: to store and process voice data locally or at the edge, which means closer to the device used. Beside keeping data secure, this would also solve latency issues slowing down the processing of data stored in a remote cloud server.
Spiki’s voice command recognition keeps your data locally and guards your privacy.
Relying on Spiki’s robust voice command recognition, you eliminate privacy and compliance risks and keep stored data safe. Reducing latency will reduce computation time. Since our IP can run on any third-party hardware, you save time and implementation costs. Spiki’s voice recognition is offline, to keep your private details private.
It works without Internet connectivity
It ensures privacy of customer data
It is usable in noisy environments, robust and reliable to fulfil your specific requirements
It requires no additional hardware (e.g., Alexa).
Excited? Get in touch for receiving a demonstration!
Robustness in AI can be described as predictive certainty of machine learning systems. Robust machine learning systems perform just as they have been trained to, even in unfamiliar settings, and minimise vulnerability to adversarial attacks. Put in other words: a robust AI can detect if input data is meaningfully different to what it has been trained on and mitigate unintended effects. Robustness is therefore a key prerequisite for deploying AI in safety-critical settings.1 The European Commission has, to mitigate possible negative effects of AI on society, established a set of principles for secure and trustworthy AI. Core requirements such as the concepts of explainability of AI systems and the aforementioned robusntess will also feature in future regulations of such technologies alongside the cybersecurity of digital systems and the protection of data.
When is a machine learning system robust enough for the real world?
Imagine a system for image classification that has to determine whether the pictures or bits of pictures show cats or dogs. If slightly altered pixels, shaded spots or distorted angles of the input picture lead to a completely wrong classification, this modification is called an adversarial example. If AI models make mistakes they should not, this is the exact opposite of robustness. While funny with the cats and dogs example, this cannot stand in the real world, where, for example, an autonomously driving vehicle needs to clearly distinguish street signs and obstacles.
Robustness implies that even with perturbed inputs, i.e. with any possible alteration or minuscule change to the unperturbed input, the model still classifies it correctly and does its job just like the human brain would. In practise, verification frameworks are used to test robustness in real world situtions compared to training.
The goal is clear: safety and reliability.
Robustness solves the “never enough data” problem
Whenever an AI is trained for robustness the question arises: how much learning input, i.e. data, is enough to guarantee its functioning? The answer usually is – there is never enough, the number of datapoints or inputs is infinite. That is what makes training an AI costly and time-intensive. Robustness in training makes sure that the input data points are clearly defined and specified. This reduces the amount of input data to a finite number and saves both time and money.
Spiki makes AI robust and reliable
SPIKI is building brain-inspired AI you can trust. We developed an innovative neural networks training method for supervised learning that combines
Robust Neural Networks
Specification based training data for reduced data collection efforts
Built-in formal verification for explainable AI.
We are striving to build trustworthy AI, which can be deployed via microcontroller, FPGA, as a cloud service or even ASIC as a future step in our product development. You profit from an easy to use toolchain, from training to deployment, ready for third-party hardware.
Excited? Contact us!
References
1 Tim G. J. Rudner and Helen Toner, Key Concepts in AI Safety: Robustness and Adversarial Examples, Center for Security and Emerging Technology, March 2021.
2 Hamon, R., Junklewitz, H., Sanchez, I. Robustness and Explainability of Artificial Intelligence – From technical to policy solutions, Publications Office of the European Union, Luxembourg, Luxembourg, 2020
AI makes machines and devices smart and our lives easier.
Rooted in 20th century computer science, AI can be defined as systems capable of rational and autonomous reasoning, decision-making and action. Simply put, AI is an automated decision system. The concept of AI is linked directly to machine learning or deep learning. Through AI, machines and devices are made smart: they do exactly what we want and do not require our own active manipulation, such as pressing a button. They can also adapt to changing circumstances and thus mimick a learning human brain.
While for some people this might sound scary, the intelligence of machines is incredibly useful for us when it comes to simplifying production processes, routines, or even our personal domestic lives. Contact Spiki to find out how AI can boost your performance.
The use cases for intelligent machinery are manifold.
AI is boosting business performance. Whenever analytical thinking and corresponding actions are needed to perform repetitive tasks based on an input signal, a machine can easily do the job without ever getting tired or bored. The input signal which triggers an action can be a sound or a sensor signal, or a set of data for analysis. With AI, management of data, supply chain, warehouse and logistics can be made more efficient. Be it automotive applications, production sites with conveyor belts or smart wearables – an intelligent device outperforms human handling and simplifies routines.
AI can save lives.
In the medical field, AI is already cutting edge, too. A German research team has, for example, developed a neuromorphic hardware which can detect early signs of atrial fibrillation, thus considerably reducing the risk of a stroke.1 Sounds like science fiction? Far from it! Energy-efficient chips are integrated in wearables and constantly record ECG signal data. With such long-term observations, it is possible to detect atrial fibrillation and raise an alarm. AI can even save lives! Even after the event of a stroke, learning systems can help patients regain their speech spectrum and coordination skills. Smart devices adapt customised training programs to the individual status and progress of the patient – what a great motivation to train essential skills at home!
AI can improve our everyday lives.
AI can even save lives.
My home is my (smart) castle.
Moreover, AI can improve our own everyday lives. Autonomous driving is, admittedly, a field which still needs bulletproof safety guarantees and regulations. The integration of neuromorphic hardware into cars is well underway and will soon be able to provide fast responses to real-life traffic situations. Already today it is possible to navigate smart systems in cars, on mobile phones, wearables or at home, solely with voice commands, having your hands free. You can turn on the light, regulate your heating, initiate a phone call or simply call for help with any device, fitted to your needs.
Spiki can tell you how!
References
Read this fascinating article by Dr. Janine van Ackeren, published May 6, 2021: Dramatically reducing the risk of stroke with neuromorphic hardware. Url: shorturl.at/ioWZ5.
You know the feeling? Just bought a new, shiny smart phone but you hardly find your way through it. Endlessly clicking on icons and checkboxes makes you dizzy. You try to hit all the right buttons but it will not do what you want, and without Internet connection, it is not so smart any more.
We feel you.
This is why Spiki makes smart phones – or any smart device, for that matter – understand you. Our mission: make navigating your smart device easy for you.
Spiki develops and implements easy, intuitive voice command recognition for customer-specific digital navigation needs. Why is this even necessary?
Get out of the digital maze – with Spiki.
Did you ever end up in a digital navigation menu without knowing how you got there – or how to get out of it again? For many elderly people, navigating a phone, let alone a smart phone, is a digital maze. You have to know exactly which functions to activate and how and where to press the right buttons. Doing this with bad eyesight or clumsy fingers can be a challenge.
A recent study underlines the various barriers to smartphone and tablet use by senior citizens. Users of different versions of dedicated “senior mobile phones”, which have bigger buttons and a bigger display, frequently report that just reducing the buttons as compared to a conventional smart phone and arranging them all over the device does not make handling any easier. They still have to know where they want to navigate to and which buttons to press.
A smart phone should do exactly what you want it to. Always.
We imagined it would be much easier if phones or tablets could process spoken commands to do exactly what is required. And they should do so 100 % reliably, in any circumstances. Handling any smart device with Spiki’s digital navigation is as easy as talking to yourself. Simple commands which are intuitive and do not require Internet connection help seniors, and all other smart device owners, reclaim their confidence and feel at ease with their smart device.
Obvious commands like “call my doctor”, “switch on torchlight”, “look up weather forecast”, “open WhatsApp” or “write SMS to my daughter” are comlemented with life-simplifying functions like ringing and vibrating when asked “where’s my phone” or calling help if you call out for “help”. It goes without saying that the Spiki voice command recognition operates even in a noisy environment, or if the users suffer from speech slur.
Without needing the Internet or any extra hardware, users have full control over their mobile phone and tablet, with their hands free.
Spiki’s voice command recognition excels in many application areas.
It works without Internet connectivity
It ensures privacy of customer data
It is usable in noisy environments
It requires no additional hardware (e.g., Alexa)
The hotword detection – shown as a red dot in the following comparative chart – is four times more accurate (99.48 % accuracy) than existing methods, even restricted to a similar model size (measured in KiloBytes):
Spiki’s model clearly outperforms similar detection models such as Google Speech Commands. Spiki’s command recognition can improve ease of use and user experience over various devices and applications, such as smart watches, home applications, switches, industrial control systems, UAVs and other vehicles.
That is why we developed the Spiki approach to machine learning.
Spiki – Brain-inspired AI you can trust
Artificial Intelligence (AI) is not science fiction any more, but found in many everyday life applications such as audio, video or navigational devices like smart home applications or autonomously driving vehicles. Biometric, medical and industrial use are on the forefront. Everyday life can be simplified and more convenient using devices with voice commands or sense-and-avoid technology. Spiki has developed a new approach to machine learning which makes it easier and more cost-effective for our partners to implement fully trained neuronetworks into their systems and thus offer a superiour user experience.
Making AI trustworthy
Up-to-date AI needs to combine sensing and computing for fail-safe real-time recognition and perception. Just think of a truck that is led onto an unpassable mountain road by its navigational system… or an autonomously driving vehicle that does not detect a stop-sign. Unthinkable, right? Therefore, AI needs to be safe and reliable. Spiki offers you event -based vision sensors that feed adaptive robotics with reliable visual signals and make them react with precise responses. Speech recognition and voice command systems are the most mature applications in our portfolio. And there is no limit to other exciting use cases!
Real-time hardware, ready to use
Spiki‘s training framework and IP boost smart appliances in robotics, industrial IOT, automotive, wearables, home-automation, sensor engineering etc. We are striving for building trustworthy AI which can be deployed via microcontroller, FPGA, as a cloud service or even ASIC as a future step in our product development.
Power-optimized AI for video, audio and continuous sensor data
AI inference robust against low-quality input data
Cost reduction in data collection
Ready for third-party hardware
Real-time hardware building blocks for FPGA and ASICs
Functional and legal safety
What makes the Spiki approach better than contemporary AI solutions?
There is never enough data to train an AI to perfection. Spiki limits the amount of data needed. Costs for data collection are reduced. Our AI inference is robust against low-quality input data.
AI makes mistakes. Spiki makes AI trustworthy. We guarantee functional safety for a superior user experience, legal safety and reduced liability risks. Spiki meets the regulatory requirements for explainable AI.
Using AI is energy-intensive. Spiki’s AI helps you save energy and money. Spiki trains Spiking Neural Networks.We develop a power-optimized AI for audio, video and continuous sensor data. You profit from an easy to use SNN toolchain from training to deployment, ready for third-party hardware.
AI is too slow for real life. Spiki offers SNN inference hardware IP. We offer tailormade SNN hardware building blocks for FPGA and ASICs. This is improving yield and lowering costs for you, and you can profit from real-time hardware.
Get in touch and learn how to unlock the full potential of your appliances with Spiki’s AI you can trust.
