Artificial intelligence has been a revolutionary gainer in this technology-driven world, inking itself in every business sphere of the world. The rapid proliferation of AI use cases such as virtual assistants, facial recognition, etc. is redefining the product development stories of semiconductor companies. AI applications rely on hardware as a core enabler of innovation, especially for logic and memory functions.
The demand for hardware has never been greater with software being the star of the high tech for the past few decades. With hardware acting as a key differentiator in AI, semiconductor companies will find greater demand for their existing chips that can lead them to successful sales enablement. But there lies a huge opportunity for companies that develop novel technologies such as application-specific chipsets. Companies that materialize this niche opportunity will be positioned to capture more value from AI stack than their previous innovations.
Over the past decade, the world has witnessed the launch of an impressive new generation of architectures optimized for machine learning, natural language processing, and other AI workloads. As the need for computational resources soars exponentially, the demand will continue to rise for these new-gen AI chips that possess the following capabilities:
- High computation power and cost-efficiency
- Cloud and Edge computing to support advanced training models for computer vision and deep learning
- Material innovations that trigger high-speed computing than traditional materials
- New-gen architectures that can mimic brain cells
Semiconductor companies, tech-giants, and startups have taken this high-road already to develop platform-specific hardware architectures to capitalize on the opportunities in the market.
- Intel will roll-out a new chipset that can run embedded AI inside drones, cameras and other devices without connecting to data centers. Intel also introduced Edge AI DevCloud, a trial program for customers to test out the company’s AI processors and accelerators before buying.
- Hailo, an Israeli AI chip startup is working on a deep-learning processor that can perform up to 26 tera-operations per second (TOPS) and is highly power-efficient. This silicon chip is promising to find its way in smartphones, home devices, and even autonomous vehicles.
The race for semiconductor innovation towards AI also opens the gateway of product engineering opportunities for service providers. SPs can address a wide array of product development capability gaps for the industry giants to accelerate their time-to-market.
- Design, integration and testing services for core IC processors, embedded SoCs, GPUs, FPGAs and more
- System/board engineering
- Development, integration and testing services of middleware components to support target AI workloads
- Embedded software/Firmware development
Draup’s digital intentions track AI-based initiatives of Semiconductor companies to generate business information with high-value signals in industry/technology/account level. Real-time monitoring of digital intentions helps service providers to identify the potential opportunities across use-cases and understand industry/account level outsourcing intentions.
