arXiv subjects

Marc Gong Bacvanski

Publications and source records attributed to Marc Gong Bacvanski.

3 recordsLinked to original sources

arXiv2026

Does Feedback Alignment Work at Biological Timescales?

Feedback alignment and related weight-transport-free algorithms are often proposed as biologically plausible alternatives to backpropagation, yet they are typically formulated in discrete phases with implicitly synchronized forward and error signals. We develop a continuous-time model of feedback-alignment-type learning in which neural activities and synaptic weights evolve together under coupled first-order dynamics with distinct propagation, plasticity, and decay time constants. We show that learning is governed by the temporal overlap between presynaptic drive and a locally projected error signal, providing an analytic explanation for robustness to moderate timing mismatch and for failure when mismatch eliminates overlap. Our results show that in order for feedback-alignment-type algorithms to work at biological timescales, they must obey the same temporal overlap principle that applies to other biological processes like eligibility traces.

cs.LG↗

arXiv2025

Dense Associative Memories with Analog Circuits

The increasing computational demands of modern AI systems have exposed fundamental limitations of digital hardware, driving interest in alternative paradigms for efficient large-scale inference. Dense Associative Memory (DenseAM) is a family of models that offers a flexible framework for representing many contemporary neural architectures, such as transformers and diffusion models, by casting them as dynamical systems evolving on an energy landscape. In this work, we propose a general method for building analog accelerators for DenseAMs and implementing them using electronic RC circuits, crossbar arrays, and amplifiers. We find that our analog DenseAM hardware performs inference in constant time independent of model size. This result highlights an asymptotic advantage of analog DenseAMs over digital numerical solvers that scale at least linearly with the model size. We consider three settings of progressively increasing complexity: XOR, the Hamming (7,4) code, and a simple language model defined on binary variables. We propose analog implementations of these three models and analyze the scaling of inference time, energy consumption, and hardware. Finally, we estimate lower bounds on the achievable time constants imposed by amplifier specifications, suggesting that even conservative existing analog technology can enable inference times on the order of tens to hundreds of nanoseconds. By harnessing the intrinsic parallelism and continuous-time operation of analog circuits, our DenseAM-based accelerator design offers a new avenue for fast and scalable AI hardware.

cs.NE↗

arXiv2024

QAMNet: Fast and Efficient Optical QAM Neural Networks

The energy consumption of neural network inference has become a topic of paramount importance with the growing success and adoption of deep neural networks. Analog optical neural networks (ONNs) can reduce the energy of matrix-vector multiplication in neural network inference below that of digital electronics. However, realizing this promise remains challenging due to digital-to-analog conversion: even at low bit precisions $b$, encoding the $2^b$ levels of digital weights and inputs into the analog domain requires specialized and power-hungry electronics. Faced with similar challenges, the field of telecommunications has developed the complex-valued Quadrature-Amplitude Modulation (QAM), the workhorse modulation format for decades. QAM maximally exploits the complex amplitude to provide a quadratic $O(N^2) \to O(N)$ energy saving over intensity-only modulation. Inspired by this advantage, this work introduces QAMNet, an optical neural network hardware and architecture with superior energy consumption to existing ONNs, that fully utilizes the complex nature of the amplitude of light with QAM. When implemented with conventional telecommunications equipment, we show that QAMNet accelerates complex-valued deep neural networks with accuracies indistinguishable from digital hardware, based on physics-based simulations. Compared to standard ONNs, we find that QAMNet ONNs: (1) attain higher accuracy above moderate levels of total bit precision, (2) are more accurate above low energy budgets, and (3) are an optimal choice when hardware bit precision is limited.

cs.ET↗