Goswami explained how this innovation fundamentally changes how AI algorithms are executed. “In all training processes, the core mathematical operation is vector-matrix multiplication,” Goswami said. “On a digital platform, multiplying a vector of size n by an n x n matrix takes n² steps. In contrast, our accelerator executes this in a single step. This reduction in computational steps directly translates to a substantial gain in energy efficiency.”
The energy efficiency of the new platform is especially impressive. According to a comparison cited by Goswami, the platform’s dot product engine delivers 4.1 TOPS/W, making it 460 times more efficient than an 18-core Haswell CPU and 220 times more efficient than an Nvidia K80 GPU, which is commonly used in AI workloads.
The rise of neuromorphic computing
Neuromorphic computing is an advanced field of computing that mimics the architecture and processes of the human brain. Instead of using traditional digital methods that rely on binary states (0s and 1s), neuromorphic systems utilize analog signals and multiple conductance states to process information more like neurons in a biological brain.
At the heart of IISc’s innovation is the platform’s ability to handle 16,500 conductance states. To represent more complex data, these systems must combine multiple binary states, which increases the time and energy required for processing.
“With our approach, a single device can store and process data across 16,500 levels in one step,” Goswami said. This makes the process highly space-efficient and allows for parallelism in computation, which speeds up AI workloads significantly.
These systems are designed to perform tasks such as pattern recognition, learning, and decision-making more efficiently than conventional computers. By integrating memory and processing into a single unit, neuromorphic computing promises faster, more energy-efficient solutions for complex tasks such as AI, particularly in areas like machine learning, data analysis, and robotics.