Researchers have developed a new light-based computing scheme that uses a photonic integrated circuit to reduce the energy necessary for crypto­currency and blockchain applications. Mining crypto­currencies like Bitcoin consumes up to 1 percent of the world’s energy. This energy expenditure is expected to grow as crypto­currency and blockchain appli­cations become increasingly mainstream. 

“Currently, crypto­currency mining is only accessible to those that have access to highly discounted energy – below $0.05/kWh,” said Sunil Pai, who performed the research at Stanford and is now at the quantum computing company PsiQuantum. “Our low-energy chips will make it possible for individuals all over the world to participate in mining profitably.” The researchers call their new scheme LightHash, which uses a photonic inte­grated circuit to create a photonic blockchain. With further development, the researchers predict that this approach, if implemented on a large scale, could produce a roughly ten-fold improvement in energy use compared to the best modern digital electronic processors. David A.B. Miller co-led the Stanford Univer­sity research team with Shanhui Fan and Olav Solgaard.

“Our approach to photonic blockchain could also be used for appli­cations beyond crypto­currency such as securely transferring data for medical records, smart contracts and voting,” said Pai. “This work paves the way for low-energy optical computing, which, ultimately, can reduce data centers’ energy consumption.” Growing concerns about the large amount of energy required to mine crypto­currencies have caused some popular ones such as Ethereum to shift to unproven and potentially insecure schemes to minimize their carbon footprint.

To find a more eco-friendly approach while maintaining a high level of security, Pai and colleagues use silicon photonics to reduce the energy require­ments of crypto­currency networks. LightHash improves upon a scheme the team previously developed called HeavyHash that is currently used in crypto­currency networks such as Optical Bitcoin and Kaspa. “The major motivation for LightHash was HeavyHash’s high sensitivity to hardware error,” said Pai. “Since analog computers, including photonic ones, struggle to achieve low error rates, we designed LightHash to maintain all the security properties of HeavyHash, while improving its robustness to error.”

Securely creating Bitcoin or operating its computing network requires computing a hash function like SHA256 or Heavyhash to transform input data into a single output number in a way that is too complex to be undone, which accounts for the bulk of Bitcoin’s energy use. In the new work, the researchers modified Heavyhash to work with a co-designed silicon photonic chip carrying a 6x6 network of pro­grammable inter­ferometers. This enabled low-energy optical processing of matrix multi­plications, which forms the bulk of the computation in Lighthash. To evaluate the feasibility of using LightHash for matrix multi­plication, the researchers built an optical rig to control and track the propa­gation of light by tuning heating elements and imaging grating spots onto an infrared camera. They also implemented an error mitigation algorithm and established feasibility criteria for scaling the technology.

The experimental results achieved with the silicon photonic chip matched those obtained using simulated error predictions. “Our results suggest that LightHash can be feasibly computed at scale using current silicon photonic chip technology,” said Pai. “Essentially, we have devised a way to use analog optical circuits to perform multi­plications at near zero power dissi­pation yet precisely enough for use in a digital encryption scheme.” For LightHash to demonstrate considerable advantages over digital equivalents, it must be scaled up to 64 inputs and outputs. The researchers are also working to further reduce energy consumption by designing low-power electro­mechanical tuning elements and energy-efficient converters to turn the optical signals into electrical signals.

They say that because the new chip accelerates matrix multiplication, the most computa­tionally intensive operation for AI appli­cations, it could also help make training and appli­cation of photonic neural networks more energy efficient compared to conventional digital implemen­tations. “It will be interesting to see how crypto­currency technology evolves and to what extent photonics can contribute to the increa­singly mainstream role of decentra­lized ledgers in society today,” said Pai. (Source: Optica)

Reference: S. Pai et al.: Experimental evaluation of digitally verifiable photonic computing for blockchain and cryptocurrency, Optica 10, 552 (2023); DOI: 10.1364/OPTICA.476173

Link: Ginzton Lab, Dept. of Electrical Engineering, Stanford University, Stanford, USA