Curbing Soaring Power Demand Through Foundation IP

By Bernard Murphy | January 21, 2026
Categories: AI, Automotive, EDA, IoT, IP, Synopsys

Introduction: Power Is the New Constraint

Power consumption has become one of the most pressing challenges in modern electronics. From AI-driven datacenters reshaping regional energy economics to battery-powered consumer and IoT devices struggling to extend operating life, the demand for higher performance continues to collide with strict power budgets. Without fundamental advances in enabling technologies, innovation risks being throttled by energy cost, thermal limits, and sustainability concerns.

At the heart of addressing this challenge lies foundation IP—the embedded memories, logic libraries, I/Os, and non-volatile memories that underpin every system-on-chip. Power-optimized foundation IP is emerging as a critical lever to rein in energy consumption without sacrificing performance, area, or cost.

Why Foundation IP Matters for Power

Power is uniquely difficult to optimize because it spans every layer of design—from application software and system architecture down to transistor-level implementation. While software, architecture, and RTL teams all contribute to power reduction, their success depends heavily on the efficiency of the underlying IP.

With over two decades of experience across multiple technology nodes, Synopsys Foundation IP teams—working closely with system and EDA groups—have focused on optimizing these building blocks to support aggressive power goals. A recent digest of Synopsys research highlights several key techniques that illustrate how power savings are being achieved in practice.

Low-Voltage Operation: Cutting Power at the Source

Dynamic power scales with the square of supply voltage, making voltage reduction one of the most effective power-saving strategies. In ultra-low-power designs, such as IoT and medical devices, operating voltages can drop to 0.7V or lower—and in some energy-harvesting applications, as low as 0.4V.

Conventional foundation IP struggles at these levels. Synopsys addresses this with:

• Low-voltage-optimized embedded memory compilers using multiple assist techniques

• Carefully redesigned logic cell libraries characterized for variability and near-threshold behavior

• Advanced statistical timing models that account for higher-order variations

These approaches have demonstrated 10–30% area savings and 19–37% power reductions, while supporting architectural power states ranging from light sleep to full power-off.

Managing Power for HPC and AI Workloads

At advanced nodes such as 2nm and 3nm, power optimization becomes highly application-specific. High-performance computing and AI workloads often require tailored trade-offs between speed, voltage, and thermal behavior.

The Synopsys High-Performance Core (HPC) Design Kit enables:

• Custom tuning of logic libraries for specific performance or power goals

• Support for wide Vt ranges and DVFS boost modes

• Optimized cache memory instances with tight timing margins

For AI accelerators, where dense arrays of MAC units dominate area and power, pitch-matched logic cells and memories help minimize interconnect power and improve overall efficiency.

Power-Efficient AI Inference

While AI training consumes enormous power, inference dominates operational cost in datacenters. Each inference request translates directly into energy expenditure, making power efficiency a business-critical metric.

Key foundation IP innovations supporting low-power AI include:

• Low-voltage core operation (~0.7V) to limit power density

• Specialized I/O cells that bridge low internal voltages to higher external interfaces

• Sparse-aware memory features, such as Word-All-Zero detection, which eliminate unnecessary computations

• Latch-based compact memories optimized for activation and pooling operations

These techniques directly reduce energy per inference while maintaining throughput.

Custom IP for Ultra-Aggressive Targets

Some applications push beyond standard offerings. In one example, an edge AI system for optical networking required logic and memory to operate at just 0.4V. Synopsys delivered custom memory compilers and logic libraries on an aggressive schedule, enabling the customer to meet both power and time-to-market objectives.

What’s Next: Advanced Memories and GAA

As scaling continues, foundation IP must evolve further:

• MRAM for high reliability and performance, supporting densities up to 128Mb

• RRAM for low-cost, high-density embedded applications (currently in development)

• Gate-All-Around (GAA) transistor-based IP, with Synopsys already sampling 2nm GAA libraries

These advances will be essential as the industry approaches angstrom-scale processes.

Conclusion

Rising power demand is no longer a secondary concern—it is a defining constraint on innovation across AI, automotive, IoT, and datacenter markets. Power-optimized foundation IP plays a central role in addressing this challenge, enabling system-level efficiency gains that ripple upward through architecture and software.

By combining deep process expertise, advanced modeling, and application-aware customization, foundation IP is becoming one of the most effective tools for curbing power growth while sustaining performance and scalability in next-generation designs.