Top 10 Best AI Accelerators And Startup Incubators To Follow In 2022

Top 10 Best AI Accelerators And Startup Incubators To Follow In 2022

Let’s start with an explanation of what an AI accelerator is.

A dedicated processor designed to speed up machine learning computations is known as an AI accelerator. Machine learning, especially its subset, deep understanding, is essentially made up of many linear algebra computations (i.e. matrix-matrix and matrix-vector operations), which can be easily parallelised. AI accelerators are specialised hardware meant to boost performance, reduce latency, and lower the cost of implementing machine learning-based systems by speeding up these basic machine learning operations.


Is it necessary to use an AI accelerator for ML inference?

Let’s pretend your software application includes an ML model. The prediction (or inference) stage is frequently the most time-consuming aspect of your software, and it has a direct impact on the user experience. Users may abandon your “sluggish,” “slow,” or “frustrating to use” software if it takes hundreds of milliseconds to generate text translations, apply filters to images, or make product suggestions.

You may lower total application latency and create an app experience that can be described as “smooth,” “snappy,” and “delightful to use” by speeding up inference. In addition, outsourcing ML model prediction computation to an AI accelerator helps speed up inference.

With the tremendous market demands come many product options, so there’s more than one method to speed up your machine learning models in the cloud. It can be challenging to choose the correct type of hardware acceleration for your workload. 


A little bit of hardware accelerator history

To speed up arithmetic computations on your computer in the early days of computing (the 1970s and 1980s), you combined a CPU (Central Processing Unit) with an FPU (floating-point unit), aka math coprocessor. The concept was simple: offload complicated floating point mathematical operations from the CPU to a specially built chip, allowing the CPU to focus on the rest of the application programme, running the operating system, and so on. The plan was referred to as heterogeneous computing since it contained two types of processors (the CPU and the math coprocessor).

In the 1990s, CPUs became faster, better, and more efficient, and they began to have integrated floating-point technology. Coprocessors and heterogeneous computing went out of favour with the average user, and the more straightforward system won out.

Specific types of workloads began to become more sophisticated around the same time. Engineers and scientists sought faster computers for data processing, modelling, and simulations, while designers demanded better graphics. This meant a demand (and a market) for high-performance processors that could accelerate “special applications” far faster than a CPU, allowing the CPU to focus on other tasks. Computer graphics was one of the first workloads to be offloaded to a dedicated processor. The venerable GPU is the usual term for this particular processor.

In the early 2010s, a new class of workloads — deep learning, or machine learning with deep neural networks — emerged that, like computer graphics, required hardware acceleration to be successful. GPUs were already on the market, and unlike the early GPUs, which were fixed-function computers, they have grown very programmable over time. Machine learning practitioners quickly adopted gPUs to speed deep learning training and inference.

The current state of deep learning inference acceleration is even more intriguing. Advanced vector extensions (AVX-512) were added to CPUs to speed up matrix math computations, typical in deep learning. GPUs now have new features, like support for reduced precision arithmetic (FP16 and INT8), which speeds up inference even further.

You now have access to specialist hardware, such as purpose-designed silicon explicitly intended for deep learning inference, in addition to CPUs and GPUs. These customised processors, also known as Application Specific Integrated Circuits or ASICs, can be significantly more performant and cost-effective than general-purpose processors if the processor supports your workload. AWS Inferentia, a custom-designed ASIC by AWS for accelerating deep learning inference, is a beautiful example of such specialised processors.

It’s possible that the best hardware acceleration for your application isn’t immediately apparent. The merits of each strategy and issues such as throughput, latency, cost, and other aspects that will influence your decision will be discussed in the following section.



AI accelerators and how to choose the correct option


General questions like “is GPU better than CPU?” or “is CPU cheaper than a GPU?” or “is an ASIC always quicker than a GPU?” are difficult to answer. There isn’t a single hardware option that works well for every use case. Thus, the answer is contingent on your workload and a few factors:

1. Model size, custom operators, and supported frameworks are all examples of model type and programmability.

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2. Set goals for throughput, latency, and cost: provide an excellent client experience while staying under budget.

3. Compiler and runtime toolchain ease of use: should have a short learning curve and require little hardware knowledge.


While objective factors like model support and target latency are essential, the comfort of use is subjective. As a result, we advise you to avoid making broad recommendations that don’t consider all of the above factors for your specific application. Such high-level advice is prone to be skewed.



What is a startup incubator?


The majority of new businesses don’t start with a well-defined workforce. Tech companies require considerably fewer workers to work on prototypes than traditional businesses. They are particularly prone to failure in their early years due to a lack of talent and creative diversity. Especially when the founder – or co-founders – lack the essential management or people skills to steer the boat in the desired direction effectively. A startup incubator can greatly aid a new business in acquiring traction in a fraction of the time — and cost — that would be required otherwise.

A startup incubator is a corporation that assists freshly formed and early-stage startup businesses in developing their business around their idea, product, or service. A startup incubator accomplishes this by providing services such as management training, a workspace, and all that comes with it, allowing these companies to focus on their product and business during their early phases.


Why do startups need an incubator?

In a tech startup, a full-fledged operation is uncommon. A small team of developers will frequently work on a prototype as a Proof of Concept. A business-minded co-founder or a marketing officer might be involved, but they won’t have a chance to show their worth until later in the process.

None of the original members knew what kind of dreams they had. Mentorship, more expertise than they can afford, and a network of partners or providers to help them acquire visibility are all things they’ll need. It will be difficult, if not impossible, to get any outcomes without at least some of these aspects.


  • Structured business and a clear focus

The road to success is winding and full of twists and turns. Maintaining a business focus when things get tough and the team’s morale appears to be at an all-time low is critical. A startup incubator can provide a much-needed business structure while also lowering the number of fronts on which the team must improve. That way, the founding team can concentrate on the most critical task at hand: staying afloat until their business plan begins to make sense.

A startup incubator provides a complete business infrastructure and a distraction-free work environment. It will be easier to achieve corporate goals and objectives this way. The team may prove their idea in the market, getting a step closer to success by following the less travelled path.


  • Funding

The majority of startup incubators collaborate with others. Some of these partners are also investors, an essential facet of the company. Raising funds for an unrealised vision is difficult. As a result, bringing on angel investors, potentially as part of an angel network, is the simplest method to conduct business at that point. The incubator will assist the team in completing their applications and learning how to present their idea — or product effectively.

A business incubator can connect startups with investors, allowing them to take advantage of funding possibilities as they arise. Typically, an angel investor will assist a firm with much more than money. Angel investors usually specialise in a specific industry or area, and they seek out chances to minimise the risk of failure. As a result, they can frequently assist startups by providing them with some of their expertise and contacts from their network to help them get started.


  • Workspace and equipment

A clean, well-lit, temperature-controlled, and noise-free work environment is required as part of an entire corporate infrastructure. Then there are the computers, servers, equipment, furnishings, and comfortable chairs for all of the long hours the crew will be working on their product. Not to mention the importance of having access to a meeting room, a contact centre, and a printer.

A startup accelerator should provide a complete work experience in all aspects that matter. It should also be able to do it cheaply, with variable options — or plans — to meet the team’s needs while staying within the budget.


  • Administrative, financial, or legal services at a low cost

Administrative activities, operations, IT consulting and support, financial management, and legal services are required of any business. A newly established startup company has not yet reached the stage to meet such requirements. Not in terms of the talents required, and certainly not in terms of the expenses incurred. A startup accelerator can help with this. A startup incubator provides a nearly noise-free atmosphere for the startup to research their market and create their product by giving low-cost professional services as part of the incubation programme.


  • Resources

A group of developers with a fantastic idea or a new digital startup don’t have the professional tools they need to build and grow a business and a robust customer base. A business incubator will provide the necessary training and resources to keep things moving ahead. To that aim, regular workshops on roadmaps, market research and forecasts, business basics, financial and legal frameworks, and funding are beneficial. Most significantly, all of these materials are accessible from one location. The staff no longer needs to spend time searching for resources online or travelling to obtain vital training in new skills and technology, which, of course, saves money.

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  • Partnerships

A proof of concept, a beta, an MVP, and a ray of hope; still a long way from the amount of growth required for success. A startup can gain enough traction, momentum, and confidence to develop the appropriate solution. However, nothing beats forming a solid alliance with industry professionals, influencers, or analysts. Professionals in the industry will create the network that will propel a fledgling company into healthy economies of scale, increasing by the minute, if not exponentially. Bringing on one B2C customer generates far less revenue than one B2B customer. Let alone bring on a partner who sells en masse to their B2C consumers.

In terms of know-how transfer, specialised hardware or software provision, expertise, office or lab space, or assets, partnerships — and networks — might arise. The majority of the time, strategic alliances are formed. It’s just as critical to do things more efficiently to avoid single points of failure. And nothing beats a joint venture when it comes to getting things done quickly.

Furthermore, having a network of partners will provide you with contacts, referrals, and visibility, as well as new business prospects in general.



Startup incubators aid economic development.

According to the NBIA, this is also true in regional or national economies. There are five different types of startup incubators:


1. Academic institutions

2. For-profit property development ventures

3. Venture capital firms

4. Non-profit development corporation

5. Combinations of the above


The first business incubator was established in the United States in 1959, when Joseph L. Mancuso launched the Batavia Industrial Center in Batavia, New York, in a warehouse. In the 1980s, business incubators grew in popularity, spawning a few similar concepts that eventually expanded across Europe. These included ideas like technology or science parks, innovation centres, etc.


Top 10 Ai Accelerators And Startup Incubators To Follow In 2022



1. Digital Catapult


The Digital Catapult encourages the early adoption of artificial intelligence, immersive, and future networks technology to make UK businesses more competitive and productive and expand the country’s economy. Digital Catapult bridge the gap between small and large enterprises, government, and academia to find novel solutions to real-world problems in the manufacturing and creative industries.

They provide physical and digital facilities that would otherwise be unavailable to smaller businesses, allowing them to overcome technological constraints. Digital Catapult are continuously seeking great people to join their team at Digital Catapult. Through the application of modern digital technologies, they assist in developing innovative goods, services, experiences, and business models that boost the UK economy.

Digital Catapult has a lot of experience applying artificial intelligence to new systems and operations. Machine learning, edge computing, and general adversarial networks are among the company’s specialities. Digital Catapult is also known for bringing the finest businesses together and using digital technologies to accelerate new possibilities. The company collaborates with various entities, including academic startups, technological companies, government agencies, and research institutes.


2. Y Combinator


Y Combinator is a venture capital firm that invests in businesses. The company’s primary purpose is to help entrepreneurs get over the first phase and to the point where they can come up with something new and cutting-edge. The corporation claims to be a rationalist in the sector, and it inevitably wants to promote several AI startups and organisations in each field. The company’s most significant role is collaborating with startups on their ideas, advertising and experimenting with them to develop more revolutionary products.

Y Combinator is a technology startup accelerator in the United States founded in March 2005.

It has been used to launch almost 3,000 satellites. Stripe, Airbnb, Cruise, PagerDuty, DoorDash, Coinbase, Instacart, Dropbox, Twitch, Flightfox, and Reddit are among the companies. By January 2021, the top YC firms had a more than $300 billion combined valuation. The company’s accelerator programme began in Boston and Mountain View, moved to San Francisco in 2019 and has been entirely online since the COVID-19 epidemic broke out. In 2012, Forbes named the company one of Silicon Valley’s most successful startup accelerators.



3. Antler


Antler focuses on reimagining companies in the future. The team is looking for the proper people that understand early-product market fit validation and pre-seed capital because it boosts a new company’s chances of making money. Throughout their journey, Antler startups remain a part of the network of early-stage startup founders, allowing business leaders to connect and discuss ideas with early-stage founders from around the world.

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Antler is a worldwide early-stage venture capital business that backs tomorrow’s most critical technological firms. The firm has offices in most major entrepreneurial hotspots worldwide, including London, New York, Singapore, and Sydney. In 2017, the company was founded in Singapore. Antler is on a mission to fundamentally transform the world by empowering and investing in its most remarkable people as they create tomorrow’s defining enterprises.

Since its inception, Antler has invested in over 300 businesses. At least one female co-founder is present in 40% of these businesses, and the founders represent 70 different nationalities. By forming complementary co-founder teams, providing rigorous business model validation, and providing a worldwide platform for expanding their firms, Antler empowers extraordinary people to create meaningful digital startups.





4. Analytics Ventures


Analytics Ventures is a venture studio fund committed to launching new businesses that aim to leverage artificial intelligence’s potential. The firm is backed by a group of seasoned AI scientists that have created a comprehensive venture ecosystem that comprises AI experts, operations team, and a fund. Analytics Ventures provides world-class AI expertise to organisations, researchers, and startups.

Analytics Ventures is a venture studio fund providing front-to-end infrastructure to ideate, form, launch, and fund brand new companies in artificial intelligence (AI). Analytics Ventures takes companies from formation to public launch in as little as six months with its own in-house AI lab, technology, back-office, and marketing setup. The fund’s ecosystem is supported by a vast network of corporate and academic relationships and other venture funds and was recently named Venture Capital Firm of the Year by the Global Annual Achievement Awards for Artificial Intelligence.

To present, Analytics Ventures has raised a total of $20 million for its studio and portfolio companies, including CureMetrix, a world-class provider of FDA-cleared AI-based mammography triage software.


5. AI Seed


AI Seed specialises in providing pre-and post-investment support for budding artificial intelligence and machine learning firms. To help more AI entrepreneurs succeed, the company offers access to the advice of some of the world’s most influential AI entrepreneurs. The firm has a strong network and also provides business development training.

Each year, they invest £100,000 in up to 20 early-stage entrepreneurs that use artificial intelligence and machine learning to create outstanding products and businesses that tackle real-world problems.

AI Seed expect to obtain 5-10% equity in exchange for their investment and can lead rounds and assist entrepreneurs in raising more money by making introductions.



6. AI2 Incubator


AI2 is a non-profit research centre that was established in 2014 with the goal of undertaking high-impact AI research and engineering for the more significant benefit. AI2 is managed by Dr Oren Etzioni, a famous AI researcher, founded by the late Paul Allen, philanthropist and Microsoft co-founder.

AI2 is based on the banks of Lake Union in Seattle, and it employs some of the world’s top scientific and engineering talent in the field of AI, attracting people with diverse interests and backgrounds from all over the world. AI2 is proud of its team’s diversity and teamwork, and it approaches complex AI challenges in a results-oriented manner.

AI2 has embarked on several ambitious projects to use AI to propel fundamental improvements in science, medicine, and conservation. 



7. Hero House 


Hero House is an entrepreneurship hub that combines the unique potential of top local institutions with the knowledge and connections of industry leaders in entertainment, aerospace, bioscience, and other sophisticated technology. Hero House has also formed SmartGateVC, an in-house venture capital fund.





NextGrid aims to accelerate innovation by investing in artificial intelligence-driven startups, talent, and ecosystems. The business has also developed the AI Slingshot programme to improve AI acceleration capabilities, which helps early-stage AI startups expand and succeed steadily.

Intelligent apps powered by AI are the future of software, and it’s already happening!




9. AI Venture Labs (AIVL)

The objective of AIVL is to make a big difference in the world by accelerating cloud-based AI businesses with the potential to disrupt and upend sectors. The company’s revolutionary industry laboratories assist potential AI firms in enhancing growth and productivity with one-of-a-kind acceleration.

AIVL aims to positively impact the entire world by accelerating cloud-based AI businesses with the ability to disrupt and upend sectors vertically. They do this through one-of-a-kind acceleration method and their cutting-edge Industry Labs, which were created to help potential AI startups scale and realise their vision.startup

10. Nvidia Inception


Nvidia Inception is a startup accelerator programme that provides vital market support, knowledge, and technology to cutting-edge entrepreneurs. From the perspective of reality, Nvidia investigates the possibilities of global startups through better computing.

With improvements in AI and data science, Inception promotes devoted and outstanding entrepreneurs redefining industries. This virtual accelerator programme assists startups with product development, prototyping, and implementation during vital stages.

Every firm receives a unique mix of continuous perks, ranging from hardware funding and marketing assistance to deep learning specialist training.

Edited and published by Ashlyn Joy

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