Performance Metrics

Acceleration of Matrix Multiplications

Matrix multiplication is fundamental to numerous AI algorithms, including deep learning and neural networks. JEF enhances the speed and efficiency of these operations, achieving lightning-fast processing times that previous systems could not match. This acceleration translates into quicker training times for AI models, allowing researchers and companies to iterate faster and innovate more rapidly.

Details on Optical Core vs. GPU Performance

A key advantage of the optical core is its ability to utilize each byte of data for thousands of operations, compared to traditional GPUs where a single byte may only be utilized for a limited number of operations. This capacity enables multi-PetaOp/s performance, representing a new standard in computational power. With significantly higher utilization rates, JEF ensures that resources are fully leveraged, providing exceptional return on investment (Flops/$) and energy efficiency (Flops/Watt).

Energy Consumption and Cost Efficiency

Energy efficiency is paramount in modern computing. JEF utilizes less energy than traditional systems, making it not only cost-effective but also environmentally friendly. This aspect is critical for companies seeking to reduce their carbon footprint while enhancing computational power. By facilitating the next generation of multi-die 3D systems, JEF sets the stage for sustainable computing practices.