Stanford Researchers Develop Energy-Efficient Photonic Circuits for Cryptocurrency Mining and Blockchain

  • Sergey Maga
  • 27 April, 2023 13:44
Stanford Researchers Develop Energy-Efficient Photonic Circuits for Cryptocurrency Mining and Blockchain

Researchers at Stanford University have developed a light-based computing scheme that uses photonic integrated circuits to significantly reduce the energy consumption associated with cryptocurrency mining and blockchain applications. The groundbreaking technology could make crypto mining more accessible to individuals around the world while also reducing its environmental impact.

The new method, called LightHash, leverages a photonic integrated circuit to create a photonic blockchain, which is detailed in a paper published in Optica. The team, led by Sunil Pai, estimates that this photonic blockchain could result in a ten-fold improvement in energy use compared to the best modern electronic processors when implemented on a large scale.

Currently, cryptocurrency mining is only profitable for those with access to highly discounted energy. The introduction of energy-efficient chips could democratize the process, allowing more people to participate in mining profitably. The technology could also be applied to other areas, such as securely transferring data for medical records, smart contracts, and voting.

LightHash uses silicon photonics to maintain high levels of security while reducing energy requirements. It builds upon HeavyHash, another scheme developed by the team, which is already being utilized by cryptocurrency networks like Optical Bitcoin and Kaspa.

The Stanford researchers modified HeavyHash to work with a co-designed silicon photonic chip containing a 6×6 network of programmable interferometers. This innovative design allows low-energy processing of matrix multiplications, which form the majority of LightHash computation.

In addition to its applications in cryptocurrency and blockchain, the potential of photonic circuits extends to artificial intelligence applications, offering more energy-efficient matrix multiplication processes.

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