Custom silicon for AI inference represents one of the most capital-intensive bets in the semiconductor industry. Whether you're designing NPUs for autonomous vehicles, accelerators for robotics, or efficient inference chips for IoT devices, your chip architecture innovations deserve patent protection.

The Stakes in AI Silicon

Designing custom AI chips requires massive investment—often hundreds of millions of dollars before you tape out your first production silicon. That investment creates innovations across multiple domains: novel compute architectures, memory hierarchies optimized for neural network access patterns, power management techniques, and software toolchains that make your hardware accessible.

Patents protect these innovations. More importantly in the semiconductor industry, they provide defensive leverage. Major chip companies maintain large patent portfolios partly for offensive use, but primarily as bargaining chips in cross-licensing negotiations.

Patentable Innovation Areas

• Compute architectures: Novel processing element designs, dataflow architectures, or sparsity-aware computing structures
• Memory systems: Innovations in on-chip memory, novel caching strategies for neural network workloads, or memory compression techniques
• Quantization support: Hardware implementations of low-precision arithmetic that maintain accuracy
• Compiler innovations: Novel approaches to mapping neural networks onto your hardware efficiently
• Power management: Techniques for dynamic power scaling or thermal management under inference workloads
• Multi-chip scaling: Methods for connecting multiple inference chips for larger workloads

The International Dimension

Semiconductor IP has strong international dimensions. Your chips may be manufactured in Taiwan, designed in the US, and deployed globally. A comprehensive patent strategy needs to consider protection in all relevant jurisdictions—not just where you're headquartered, but where competitors might manufacture or sell infringing products.

Timing and Trade-offs

In AI silicon, the tension between publishing and patenting is acute. Academic publications establish credibility, attract talent, and can influence industry standards. But premature publication can destroy patent rights. Work with IP counsel to develop a strategy that balances your need for visibility with IP protection.