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Athos Silicon is a semiconductor company focused on building the compute foundation for safety critical autonomy and physical AI. The core idea is to move beyond the traditional model of a single monolithic system on chip that must never fail, and instead build a scalable platform that assumes faults will happen, detects them quickly, contains them, and keeps operating in a controlled way. The company’s work combines chiplet based design, structured redundancy, fast consensus checking, deterministic scheduling, and fault tolerant power and control so autonomy systems can be engineered for real world deployment at scale.In this podcast, the conversation will explore why autonomy is not only an AI performance problem but a reliability problem that becomes unavoidable when products ship in large volumes and operate for billions of miles. It will unpack how a multi domain compute architecture can cross check results and reach agreement before committing critical decisions, and how disagreements can trigger recovery paths such as retry, reset, isolation, or safe degradation. The episode will also cover how repeating a verified chiplet building block enables scaling without multiplying validation risk, and how role based execution can distribute workload and wear over time while keeping timing predictable. Finally, it will discuss why power integrity, fault containment, and redundant control are essential to prevent cascading failures, and how this architecture supports safety arguments for certification and long term trust in the field.
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