ChipWise leverages Dynamic Voltage and Frequency Scaling (DVFS) at the JVM level, powered by AI-driven hot method profiling. Our system intelligently analyzes energy consumption patterns across CPU frequencies, delivering proven energy savings through method-level optimization.
Frequency scaling in JVM
AI analyzes energy patterns
14.9% energy reduction
21.1% efficiency gain
JVM-level DVFS technology with intelligent hot method profiling for proven energy efficiency
Dynamic Voltage and Frequency Scaling applied directly within the Java Virtual Machine, enabling method-specific frequency optimization for maximum energy efficiency
AI-driven analysis of energy consumption patterns across CPU frequencies for hot methods, identifying the optimal frequency setting for each critical code path
Advanced sampling methodology overcomes overhead challenges in energy profiling and DVFS optimization, ensuring accurate measurements without performance penalties
Seamlessly integrated into the Java Virtual Machine, providing a robust foundation for runtime optimization with comprehensive benchmark validation
Demonstrated 14.9% reduction in energy consumption compared to Linux built-in power management, with 21.1% improvement in Energy-Delay Product (EDP)
Method-level frequency selection during execution, automatically applying the most energy-efficient CPU frequency for each hot method in optimized runs
ChipWise Energy Optimizer represents a breakthrough in energy-efficient computing. Our AI-powered system leverages Dynamic Voltage and Frequency Scaling (DVFS) at the JVM level, profiling hot methods across different CPU frequencies to identify optimal energy-efficient configurations for each critical code path.
By integrating DVFS directly into the Java Virtual Machine, we enable method-specific frequency optimization that traditional OS-level power management cannot achieve. Our intelligent sampling methodology addresses the overhead challenges inherent in both energy profiling and DVFS, ensuring accurate optimization without significant performance penalties.
Our technology has demonstrated significant improvements in energy efficiency, delivering measurable reductions in power consumption while maintaining performance. As we bring this innovative solution to market, we're seeking visionary investors and early adopters to help scale this transformative technology across the industry.
Energy Reduction vs. Linux PM
EDP Improvement
Target Market Size
Ready to be part of the future of AI-driven sustainable computing? Let's discuss investment and partnership opportunities in cutting-edge artificial intelligence for energy optimization.
Ontario, Canada