Secure and Assure Semiconductor Data Movement with Arteris

AI-era SoCs are redefining what security means at the silicon level. As heterogeneous compute, shared memory systems, and chiplet-based architectures proliferate, the security perimeter extends deep into on-chip data movement, integration seams, observability paths, and configuration mechanisms.

In these systems, the interconnect fabric is no longer just a performance backbone but is now a critical security boundary where unintended access, lateral movement, and silent integrity failures can emerge.

Traditional, software-centric security approaches and late-stage testing are no longer sufficient for AI-era workloads, particularly those operating under real-time, safety, or multi-tenant constraints.

Defensible AI silicon requires enforceable hardware controls that operate at line rate, governed integration that reduces configuration and connectivity risk, and measurable pre-silicon assurance that proves security intent has been correctly implemented before tape-out

Download the solution brief to learn how Arteris solutions help you:

• Protect on-chip data movement across AI SoCs by enforcing least-privilege access, segmentation, and isolation within the interconnect fabric to reduce lateral movement and limit blast radius.
• Improve security assurance before tape-out using measurable pre-silicon analysis to validate that security intent is implemented as designed across SoCs and chiplets.
• Reduce integration and configuration risk through governed SoC assembly, registers, and observability that minimize defects, drift, and security escapes.
• Preserve performance and predictability under load by applying quality of service (QoS), isolation, and deterministic interconnect behavior to avoid contention-driven jitter and late-stage surprises.
• Strengthen operational resilience and recovery using structured integrity mechanisms such as parity, double error detection (DED), and single error correction, double error detection (SECDED) across control paths, data paths, and last-level cache.
• Support AI systems from data center to edge by addressing the distinct security challenges of cloud-scale infrastructure and physically exposed edge and embedded deployments.