Clika is revolutionizing the world of AI models by building a platform that accelerates their performance. Founded by Ben Asaf and Nayul Kim, Clika aims to remove the roadblocks that hinder software engineers and firms from deploying AI models into production. With their toolkit, companies can downsize their AI models, reducing compute power consumption and speeding up inferencing. Clika utilizes techniques like quantization to shrink models without compromising their performance. In a market where supply chain issues are causing hardware shortages, Clika’s innovative approach to compressing AI models sets them apart from competitors. Furthermore, they have already secured $1.1 million in pre-seed funding, showing promising investor support. Clika’s future plans include pursuing seed funding and expanding their reach in the AI industry.
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Background and Inspiration
Ben Asaf’s experience at Mobileye
Ben Asaf, the founder of Clika, gained valuable experience working on dev infrastructure at Mobileye, an autonomous driving startup that was acquired by Intel in 2017. During his time at Mobileye, Asaf focused on accelerating AI model training at Hebrew University. This experience allowed him to develop expertise in MLOps (machine learning operations), which involves streamlining the process of deploying, maintaining, and monitoring AI models in production.
Inspiration for launching Clika
Asaf was inspired to start Clika after realizing that many companies and individuals lacked industrial experience in implementing MLOps into their AI development pipeline. He saw an opportunity to remove the roadblocks that software engineers and firms faced when deploying AI models. Asaf wanted to make AI more accessible, affordable, and efficient in order to facilitate its widespread productization and commercialization.
Introduction to Clika
Clika’s purpose and objectives
Clika aims to provide a solution for companies seeking to optimize their internally developed AI models. The platform offers a toolkit that enables automatic downsizing of AI models, reducing the compute power required while simultaneously improving inference speed. Clika’s goal is to make the process of connecting pre-trained AI models and generating compressed models simple and hassle-free.
Participation in TechCrunch Disrupt Startup Battlefield
Clika had the opportunity to showcase its innovation and compete in the Startup Battlefield 200 competition at TechCrunch Disrupt. This event offers startups a platform to present their ideas and gain exposure in the tech industry. Participation in TechCrunch Disrupt is a testament to Clika’s potential and recognition within the startup community.
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Description of Clika’s toolkit
Clika’s toolkit provides companies with the tools necessary to automatically downsize their AI models. By leveraging techniques such as quantization, Clika can reduce the number of bits required to represent information in a model without compromising its performance. This technological approach sets Clika apart from other solutions in the market.
Functionality of the toolkit
Clika’s toolkit simplifies the process of connecting pre-trained AI models and generating compressed models. The platform ensures compatibility with various target devices, including servers, cloud infrastructure, edge devices, and embedded devices. With Clika, companies can efficiently optimize their AI models without the need for extensive manual intervention.
Compatibility with different devices
One of Clika’s strengths is its compatibility with various devices. By offering support for different hardware and environments, Clika allows companies to deploy their compressed AI models in the most suitable setting. Whether it’s on-premises or in the cloud, Clika ensures that the downsized models can be seamlessly integrated across different devices.
Techniques Used by Clika
Quantization as a technique for model compression
Clika utilizes quantization as a technique for compressing AI models. Quantization involves reducing the number of bits needed to represent information in a model. While this may sacrifice some precision, it results in a smaller model size and decreased compute power requirements. Clika’s quantization techniques ensure that the downsized models maintain their ability to perform specific tasks effectively.
Generation of performance improvement reports
In addition to model compression, Clika generates performance improvement reports for AI models. These reports highlight potential areas for improvement or changes that could enhance a model’s performance. By providing actionable insights, Clika empowers companies to continuously refine and optimize their AI models.
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Growing Interest in Efficient Models
AI hardware supply chain issues
The AI industry faces challenges related to the supply chain of hardware required to run AI models efficiently. Microsoft has warned about potential service disruptions for Azure customers due to AI hardware shortages. This scarcity of AI hardware reinforces the need for solutions like Clika, which optimize models to reduce compute power requirements.
Azure customer service disruptions
Microsoft’s recent earnings report highlighted the potential impact of AI hardware shortages on Azure customers, leading to service disruptions. As the demand for AI models continues to grow, the industry must address hardware limitations to ensure smooth operations. Clika’s focus on downsizing AI models aligns with the need for efficiency and accessibility.
Availability of Nvidia AI chips
The availability of Nvidia’s AI chips, particularly the high-performing H100 GPU series, has faced limitations, with reports suggesting stock shortages until 2024. This scarcity further emphasizes the importance of AI model compression techniques, as companies seek alternatives to ensure optimal performance without relying solely on specific hardware.
Competition in AI Model Compression
Other startups in the same space
Clika is not the only startup targeting AI model compression. Deci, backed by Intel, is one of the notable competitors in the field. OctoML, similar to Clika, also focuses on automatically optimizing and packaging AI models for different hardware environments. CoCoPie aims to optimize AI models specifically for edge devices. While competition exists, Clika believes it holds a technological advantage.
Comparison of Clika’s approach and advantages
Clika differentiates itself by taking an AI-driven approach to model compression rather than rule-based techniques employed by other solutions. By understanding the unique structures of different AI models, Clika can apply the most effective compression methods to achieve superior performance. This technological edge positions Clika as a leader in the field, surpassing the performance of existing solutions developed by Meta and Nvidia.
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Clika’s Funding and Investors
Pre-seed round and investors
Clika’s early success is evident in its pre-seed funding round, where it raised $1.1 million. Notable investors who participated in this round include Kimsiga Lab, Dodam Ventures, D-Camp, and angel investor Lee Sanghee. The support and confidence from these investors validate Clika’s potential and highlight its market viability.
Current customer base and closed beta testing
While Clika is currently in the closed beta testing phase, it has garnered interest from select businesses. Clika’s focus on delivering a high-quality product and establishing strong customer relationships positions the company for future growth and adoption.
Plans for seed funding
Clika plans to pursue seed funding in the near future, aiming to secure additional resources to scale its operations and expand its customer base. With a solid foundation and positive reception in the market, Clika is well-positioned to attract further investment that will drive its growth and development.
Clika’s potential impact on AI model compression
Clika’s platform has the potential to revolutionize AI model compression by offering an automated and AI-driven approach. The simplification of downsizing AI models while maintaining performance can unlock new possibilities for widespread adoption of AI technology across industries. Clika’s focus on efficiency and accessibility aligns with the growing demand for optimized AI models.
Future prospects and goals
As Clika continues its closed beta testing and prepares for seed funding, its future prospects appear promising. The ability to address the challenges posed by AI hardware shortages and deliver effective model compression solutions positions Clika as a frontrunner in the field. With continued innovation and strategic partnerships, Clika aims to become a driving force in the AI model compression landscape, enabling companies to deploy AI models more efficiently and affordably.
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