Advait Madhavan

May 14, 2024 | nist.gov | William Borders |Advait Madhavan |Matthew Daniels |Vasileia Georgiou

, , , Vasileia Georgiou, Martin Lueker-Boden, Tiffany Santos, Patrick Braganca, , , The increasing scale of neural networks needed to support more complex applications has led to an increasing requirement for area- and energy-efficient hardware. One route to meeting the budget for these applications is to circumvent the von Neumann bottleneck by performing computation in or near memory.

May 14, 2024 | link.aps.org | William Borders |Advait Madhavan |Matthew Daniels |Vasileia Georgiou

The increasing scale of neural networks needed to support more complex applications has led to an increasing requirement for area- and energy-efficient hardware. One route to meeting the budget for these applications is to circumvent the von Neumann bottleneck by performing computation in or near memory. However, an inevitability of transferring neural networks onto hardware is the fact that nonidealities, such as device-to-device variations or poor device yield impact performance.

Mar 15, 2023 | nist.gov | Advait Madhavan

The human brain is an amazingly energy-efficient device. In computing terms, it can perform the equivalent of an exaflop — a billion-billion (1 followed by 18 zeros) mathematical operations per second — with just 20 watts of power. In comparison, one of the most powerful supercomputers in the world, the Oak Ridge Frontier, has recently demonstrated exaflop computing. But it needs a million times more power — 20 megawatts — to pull off this feat.