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It’s easy to assume processing performance and scale are the only hurdles standing between the AI systems of today and the innovations of tomorrow. But skyrocketing power consumption is an equally critical challenge that must be addressed.
Consider this:
Historically, chip developers have treated energy consumption as a secondary constraint — optimizing performance first and addressing power concerns late in the design cycle. That approach is no longer viable.
Today, the "performance per watt" metric is just as important as raw speed, especially for AI chips and chiplets. Hyperscalers are even revising their benchmarks to focus on "tokens per watt," directly tying computing performance to power consumption and highlighting the urgency for energy-efficient computing.
Discover strategies for enhancing power efficiency in AI chip development from architecture to implementation.
Leaving power optimization until the final stages of chip design is a costly mistake. According to Synopsys customer surveys, prioritizing energy efficiency during the earliest architectural phases can yield 30% to 50% power savings, while only single-digit improvements are achievable during implementation or signoff.
The earlier power is considered, the greater the potential for meaningful impact.
A comprehensive, end-to-end approach to silicon-to-systems design enables engineers to minimize energy consumption without sacrificing performance. But four major challenges continue to vex many teams developing AI chips:
The relentless demand for AI compute is fueling the rise of multi-die designs — semiconductor devices composed of multiple homogeneous or heterogeneous dies (called chiplets) within a single package. This modularity enables rapid development of tailored silicon solutions for high-performance tasks. However, combining multiple chiplets in one package can generate concentrated heat, making thermal dissipation increasingly challenging as power density rises.
Effective thermal management starts with architectural planning. By exploring different partitioning options early and avoiding premature design lock-in, teams can minimize thermal stress and maximize energy efficiency. Modeling tools and simulation allow architects to abstract chip components and evaluate power-performance tradeoffs before finalizing the design.
AI thrives on massive memory bandwidth, fast throughput, and low latency. Yet, the growth in compute capability has far outpaced advances in memory technology — a phenomenon known as the "Memory Wall." For AI chips, one of the leading causes of power consumption isn’t computation itself, but data movement: Transferring large datasets between dies within a chip can be a significant energy drain.
To maximize performance per watt, developers must analyze data movement early and implement solutions to minimize unnecessary transfers. Strategies include adopting high-bandwidth memory (HBM), analog computing, custom compute units, compute-in-memory architectures, and advanced algorithmic techniques such as sparse algorithms. The right memory architecture — chosen and analyzed during early design phases — can dramatically reduce power consumption and boost overall performance.
Before initiating the power design cycle, it’s vital to establish a robust power analysis flow. Early-stage architecture analysis provides crucial insights into power-performance tradeoffs before hardware descriptions are finalized. This helps designers optimize hardware-software partitioning, configure system-on-chip (SoC) infrastructure, and explore advanced technologies such as dynamic voltage and frequency scaling (DVFS), power gating, and network-on-chip (NoC) traffic management.
Transaction-level simulation accelerates design by predicting and optimizing key performance indicators, allowing teams to make informed decisions that balance power, performance, and cost.
AI chips must be optimized for the specific workloads they will run. This requires modeling, simulation, emulation, and prototyping long before hardware returns from the fab. Early architecture analysis and performance validation can help improve the efficiency of hardware/software partitioning and customized solutions — whether for general AI inference or specialized applications.
Profiling a range of workloads, from idle and sustained to inference and training, enables comprehensive analysis and fine-tuning for peak efficiency. This holistic approach ensures the chip is well-matched to its intended tasks, avoiding wasted power and maximizing overall effectiveness.
The diminishing returns of late-stage power optimization highlight the necessity for power analysis and optimization at every stage of development: architecture, design, verification, emulation, prototyping, implementation, test, engineering change orders (ECO), and signoff.
But power challenges don’t end at tapeout. Over time, silicon performance can degrade due to aging and environmental factors. Silicon Lifecycle Management (SLM) offers ongoing data collection, analysis, and control to monitor and correct these effects, ensuring stable and efficient operation throughout the device’s life.
The escalating demands of AI workloads require a fundamental shift in chip development — one that prioritizes power efficiency from the very start. The complexities of multi-die designs, the energy drain of data movement, and the imperative for workload-specific optimization all underscore the need for early and continuous power analysis.
Synopsys offers a comprehensive suite of tools and proven methodologies that empower developers to establish a proactive approach and deliver high-performance, energy-efficient AI chips that meet the demands of our increasingly intelligent world.