- Semidynamics: From RISC-V with AI to AI with RISC-V
- Optimized SAR ADCs, Sigma-Delta ADCs, DACs, and Audio CODECs for IoT, MCU, SoC, and Consumer Applications
- CFX 0.13μm eFuse OTP IP has been applied in the mass production of over 15,000 CMOS image sensors
- Weebit Nano and DB HiTek to demonstrate chips integrating Weebit ReRAM at PCIM 2025
- Cadence Enables Next-Gen AI and HPC Systems with Industry's Fastest HBM4 12.8Gbps IP Memory System Solution
IP-SOC DAYS 2025 IP-SOC DAYS 2024 IP-SOC DAYS 2023 IP-SOC DAYS 2022 IP-SOC DAYS 2021 IP-SOC 2024 IP-SOC 2023 IP-SOC 2022 IP-SOC 2021
|
|||||||
![]() |
|

The Case for Hardware-Assisted Verification in Complex SoCs
- Perforce Partners with Siemens for Software-Defined, AI-Powered, Silicon-Enabled Design (May. 16, 2025)
- Semidynamics: From RISC-V with AI to AI with RISC-V (May. 16, 2025)
- TSMC Board of Directors Meeting Resolutions (May. 16, 2025)
- Arm Evolves Compute Platform Naming for the AI Era (May. 16, 2025)
- Secafy Licenses Menta's eFPGA IP to Power Chiplet-Based Secure Semiconductor Designs (May. 15, 2025)
- See Latest News>>
Mar. 17, 2025 –
By Karl Freund, Founder and Principle Analyst, Cambrian-AI Research
EETimes (March 14,2025)
Synopsys recently launched two new hardware-assisted verification (HAV) systems, intended to address the need for specialized hardware to manage the complexity of modern chip design. In this article, we look at the rationale for HAV and look into the two new systems.
The rationale for HAV hardware
In a recent interview, Frank Schirrmeister, executive director of strategic programs at Synopsys, provides some insights into large-scale chip and chiplet designs.
Designing AI accelerator chips is an exceptionally complex endeavor. Modern chips integrate billions of transistors and diverse processing units (CPUs, NPUs, GPUs, etc.), all working in parallel. Verifying such complexity with traditional simulation is daunting—it is not just for functional correctness but also for ensuring power efficiency and performance targets are met under real workloads.
New AI accelerators typically employ workload-specific optimizations (e.g. low-precision arithmetic, novel dataflows) that demand extensive architectural validation. The sheer scale of operations (often quadrillions of cycles for complete verification makes software simulation alone infeasible).