IBM Research | ICLR Paper: Learn Step Size Quantization @ibmresearch | Uploaded June 2020 | Updated October 2024, 4 days ago.
As deep networks are increasingly deployed in memory-constrained and throughput-critical systems, there is a need to create AI models that can maintain accuracy – and, as a result, trust – while also consuming fewer resources. Researchers at IBM’s Almaden Research Laboratory have reached a new milestone in AI precision and developed an algorithm that matches the inference accuracy of a 32-bit network while using only three bits.
The researchers achieved this level of energy efficiency using a new process called “learned step size quantization,” which improves parameter change estimates in a low-precision network during training, to produce better performance. The research also uncovered evidence that AI systems seeking to optimize performance on a given system might run with as few as 2 bits. This advance means AI systems are steadily coming closer to the low levels of energy consumed by the human brain, while maintaining performance.
As deep networks are increasingly deployed in memory-constrained and throughput-critical systems, there is a need to create AI models that can maintain accuracy – and, as a result, trust – while also consuming fewer resources. Researchers at IBM’s Almaden Research Laboratory have reached a new milestone in AI precision and developed an algorithm that matches the inference accuracy of a 32-bit network while using only three bits.
The researchers achieved this level of energy efficiency using a new process called “learned step size quantization,” which improves parameter change estimates in a low-precision network during training, to produce better performance. The research also uncovered evidence that AI systems seeking to optimize performance on a given system might run with as few as 2 bits. This advance means AI systems are steadily coming closer to the low levels of energy consumed by the human brain, while maintaining performance.
