The opportunities arising from the rise of generative AI- (1/n)

A technological renaissance is unfolding before our very eyes, redefining the way we live, work, and play. In a series of interesting discussions with some of the experts at Insight Monk , we got to explore the emerging opportunities beyond softwares owing to the rise of generative AI ( viz. ChatGPT). The rise of generative AI has created several opportunities in the #deeptech space, particularly in the #semiconductors sector. As AI algorithms continue to advance, the demand for more powerful and efficient hardware also increases. We list down 5 key opportunities that majority of industry experts agree upon.

1. AI-optimized chip design: Ranging from Google's TPU, a custom ASIC specifically designed for machine learning workloads, to Cerebras Systems Wafer Scale Engine (WSE), the world's largest AI chip. The WSE contains over 1.2 trillion transistors and can deliver impressive performance improvements for AI applications including generative AI applications. Google's TPUs have been optimized for the TensorFlow framework and offer significant performance improvements and energy efficiency compared to traditional CPUs and GPUs. Syntiant Corp. is focused on developing ultra-low-power AI chips for edge devices. Their NDP (Neural Decision Processor) technology combines custom analog and digital circuits to perform AI tasks with minimal energy consumption, making them ideal for battery-powered devices like smartphones, wearables, and IoT devices.
2. AI accelerators: while NVIDIA's A100 GPU, based on the Ampere architecture seems like gaining lot of traction, Mythic is also developing AI accelerators based on analog computing, which offers the potential for significant improvements in energy efficiency compared to traditional digital computing. The A100 GPU offers significant performance improvements compared to previous-generation GPUs and supports features like Multi-Instance GPU (MIG) and third-generation Tensor Cores, which are specifically designed for AI workloads. Intel has filed patents related to custom AI accelerators designed specifically for generative AI models.
3. Neuromorphic computing: Intel's Loihi, a neuromorphic research chip, uses a unique architecture designed to mimic the human brain's neuronal structure and connectivity. Some notable startups working across neuromorphic computing includes BrainChip MemComputing, Inc. GrAI Matter Labs and Applied Brain Research amongst others. Their approach aims to create scalable and energy-efficient neuromorphic systems
4. Materials innovation: Carbonics, Inc. , a startup focusing on carbon nanotube technology, has developed a new type of Field-Effect Transistor (FET) based on carbon nanotubes. This technology offers the potential for faster, more energy-efficient, and smaller semiconductor devices that can cater to the needs of advanced AI applications, including generative AI. The rise of generative AI has spurred material innovation in various fields such as 2D materials, CNT etc, as researchers and companies search for ways to improve the performance and energy efficiency of AI hardware.
5. Edge AI devices: Companies like Apple have filed patents related to integrating AI capabilities directly into edge devices, such as the Neural Engine in Apple's M1 chip. These innovations enable AI tasks, including generative AI workloads, to be performed on-device, improving efficiency and reducing latency.

While semiconductor and advanced material industry seems to have tons of positive outcomes for itself owing to splurge in generative AI, there are disruptions and opportunities alike across multiple verticals. Sign up on Insight Monk to stay updated on emerging opportunities. Also, if you are a scientist or a deep tech founder, we are happy to chat with you and profile your amazing work. drop me a quick hello at [email protected].