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Julian Büchel

2 accepted papers

2025

Analog Foundation Models

NeurIPS 2025poster

Analog in-memory computing (AIMC) is a promising compute paradigm to improve speed and power efficiency of neural network inference beyond the limits of conventional von Neumann-based architectures. However, AIMC introduces fundamental challenges such as noisy computations and strict constraints on…

Cited by 0Source ↗code ↗Scholar ↗
2022

NETWORK INSENSITIVITY TO PARAMETER NOISE VIA PARAMETER ATTACK DURING TRAINING

ICLR 2022poster

Neuromorphic neural network processors, in the form of compute-in-memory crossbar arrays of memristors, or in the form of subthreshold analog and mixed-signal ASICs, promise enormous advantages in compute density and energy efficiency for NN-based ML tasks. However, these technologies are prone to c…

Cited by 5Source ↗PDF ↗Scholar ↗