Energy Efficient Computing

Deploying eco-friendly processor architectures to power future generation AI​

THE BENEFITS OF ARTIFICIAL INTELLIGENCE

Around the world, artificial intelligence is rapidly becoming technology’s most important priority, enabling new services such as robotics and autonomous vehicles, and enhancing existing operations like telemedicine and e-learning. Governments, organizations and individuals all stand to gain from these immense benefits of AI.​

The future of AI is very bright. However, challenges abound with respect to deploying AI in a sustainable fashion.

AI requires a lot of computing power, which increases the energy demand. Predictions and particularly training often use the entire CPU of a machine - in the latter case, for an extended period of time.

The size of AI model is growing at a rapid pace – driven by the broad application of AI in everyday use that is demanding increasing model complexity.

To keep enjoying the benefits of AI while not negatively impacting our climate, we need computing systems that are efficient at crunching AI models.

HARDWARE/SOFTWARE CO-DESIGN FOR SUSTAINABLE COMPUTING

As energy demand for AI applications continues to increase, Flapmax remains committed to designing and deploying AI processor architectures that are both high performant and energy efficient.​ Our approach involves many levels of optimization and innovation, from algorithms and software to chip-level and full system. ​ ​

Algorithmic Optimization

Most AI models employ dense matrix operations - these require increased computing density, unlike sparse computations that can offer improved energy efficiency. Based on these principles we design tools to achieve more energy-efficient AI computing. Our approach involves 3 levels of optimization: model-level, kernel-level and system-level.​

Chip-level Innovation​​

With models optimized at the algorithmic level, hardware must evolve accordingly. We perform hardware re-designs simultaneously with algorithmic level optimizations while optimizing for energy efficiency and chip re-use. This flexibility allows us to use a single processor architecture for multiple workloads, recycling our hardware real-estate, reducing e-waste and improving circularity.​

Model
Optimization
Kernel
Mapping
System-level
Optimization
Chip-level
Optimization
Chip re-use
Circularity
Head With Circuitry
Microchip
Microchip
Microchip
Microchip
Microchip

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