The Next Chapter for AI ASICs: Building the Privacy Pillar of AI

By Richard Lu and Shirley Segal, Chain Reaction

Over the past decade, AI has driven a revolution in computer hardware. The rise of deep learning pushed general-purpose processors to their limits, leading to application-specific integrated circuits (ASICs) purpose-built for AI workloads (CSET, 2020) Today, these AI ASICs power everything from large language model training to real-time image recognition. As AI infrastructure has matured, growing concerns around data privacy are fueling demand for Privacy-Enhancing Technologies (PETs), opening the door for a new class of ASICs designed not just for performance, but for security and trust.

The Evolution of AI ASICs

AI acceleration began with GPUs, originally built for graphics but well-suited for parallel computation (IEEE, 2018). By the mid-2010s, hyperscalers like Google pushed further, creating processors designed specifically for AI. In 2015, Google launched the Tensor Processing Unit (TPU), optimized for massive matrix multiplications that drive neural networks‎. The success sparked a wave of industry-wide innovation. Today, AI ASICs underpin breakthroughs from real-time vision to foundation models training.

Market analysts confirm this trajectory. Gartner’s 2025 report identifies AI processors as one of four crucial technology areas shaping Hype Cycle for Generative AI (Gartner, 2025). Yole Group projects massive AI processor opportunity: $313B by 2030, (19% CAGR), with AI ASICs growing at an exceptional 45.4% CAGR – over triple GPU growth (Yole Group, 2025)

Key Players in AI ASIC

The AI hardware race spans hyperscalers, established semiconductor companies, and specialized startups. Hyperscalers such as Google (TPU), Amazon (Trainium‎‎, Inferentia‎), and Microsoft (Maia) have invested heavily in in-house designs. Among semiconductor leaders, NVIDIA continues to dominate with its H100 and H200‎ accelerators, Intel launched Gaudi 3 in 2024, and AMD launched MI350X and MI355X in 2025. Meanwhile, specialized startups: including SambaNova, Cerebras, Groq, and Graphcore, are introducing unique architecture designed to push AI performance and efficiency in novel directions (IEEE Xplore, 2022)

The Next Frontier – AI Privacy Protection

As AI adoption accelerates, data privacy is becoming a strategic priority. Regulations and consumer expectations demand privacy protection at every stage‎, driving momentum for PETs, which allow data to be used without exposure (OECD, 2024). Among the PETs, Fully Homomorphic Encryption (FHE) is often called the “holy grail” of cryptography (IEEE Xplore, 2017). FHE allows computation directly on encrypted data – such as analyzing medical records or running AI models – without ever decrypting them. This makes it possible to unlock sensitive data without compromising privacy (For a deeper dive, see Chain Reaction’s FHE Series).

The Challenges

FHE, though it offers unprecedented protection in privacy, comes at a steep cost – computations are often tens of thousands of times slower than on unencrypted data. Current CPUs and GPUs are not optimized for the unique math FHE requires, making real-time encrypted computation impractical at scale (NIST, 2022).

Cryptographic AI Accelerators

Just as AI ASICs unlocked the deep learning era, a new generation of cryptographic accelerators is emerging to make privacy-preserving computing practical. These accelerators address the growing demand for technologies that allow governments, organizations and individuals to unlock the value of private data without compromising it. Chain Reaction’s 3PU™ is one such breakthrough, purpose-built to accelerate FHE and enable real-time deployment.

The potential use cases for privacy preserving accelerator span industries where privacy is not just a preference but a requirement. In healthcare, it allows hospitals and research institutions to collaborate on patient data while ensuring full confidentiality. In financial services, it enables real-time fraud detection and risk modelling on encrypted transactions, balancing compliance with innovation. Governments and public agencies could leverage these accelerators to analyze citizen or defense data securely, fostering collaboration without compromising privacy.

Taken together, these examples highlight cryptographic ASICs as the next wave of digital infrastructure. AI ASICs provided the performance pillar of modern AI, and cryptographic accelerators will provide the privacy pillar, ensuring that innovation in AI remains on course by embedding privacy at its core. As regulatory expectations intensify and markets recognize the value of privacy-enhancing technologies, cryptographic acceleration is set to become as foundational to privacy protected AI computing as AI ASICs are to generative AI.

Conclusion

The trajectory is clear: specialized hardware redefines what’s possible. AI ASICs power today’s AI boom; Chain Reaction’s privacy preserving AI accelerator will do the same for privacy protection. Soon, businesses and individuals will no longer have to choose between extracting insights and protecting data: they will finally be able to do both.