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Analog in-memory computing is a promising platform for on-device execution of large language models because it performs matrix--vector multiplications (MVMs) in memory and in parallel, reducing data movement. However, limited digital-to-analog converter precision, input noise, and finite conductance states can degrade...
The Forward-Forward Algorithm (FFA) replaces backpropagation (BP) with layer-wise local contrastive objectives, eliminating the backward pass and the need to retain intermediate activations, yet suffers a persistent performance gap with BP that worsens with depth. This paper diagnoses two structural failure modes: an o...
This paper studies the convergence of stochastic gradient descent when the implemented updates are subject to a persistent and state-dependent bias, in which the desired update is scaled by response functions component-wise, and proposes a gradient-based algorithm, termed Residual Learning.
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