The AI Hardware Problem

▶ Check out Brilliant with this link to receive a 20% discount! The millennia-old idea of expressing signals and data as a series of discrete states had ignited a revolution in the semiconductor industry during the second half of the 20th century. This new information age thrived on the robust and rapidly evolving field of digital electronics. The abundance of automation and tooling made it relatively manageable to scale designs in complexity and performance as demand grew. However, the power being consumed by AI and machine learning applications cannot feasibly grow as is on existing processing architectures. THE MAC In a digital neural network implementation, the weights and input data are stored in system memory and must be fetched and stored continuously through the sea of multiple-accumulate operations within the network. This approach results in most of the power being dissipated in fetching and storing model parameters and input data to the arit