6 papers
mGRADE: Minimal Recurrent Gating Meets Delay Convolutions for Lightweight Sequence Modeling
Tristan Torchet, Christian Metzner, Karthik Charan Raghunathan +4
Multi-timescale sequence modeling relies on capturing both local fast dynamics and global slow context; yet, maintaining these capabilities under the strict memory constraints comm…
Unified Memcapacitor-Memristor Memory for Synaptic Weights and Neuron Temporal Dynamics
Simone D'Agostino, Marco Massarotto, Tristan Torchet +7
We present a fabricated and experimentally characterized memory stack that unifies memristive and memcapacitive behavior. Exploiting this dual functionality, we design a circuit en…
Quantizing Small-Scale State-Space Models for Edge AI
Leo Zhao, Tristan Torchet, Melika Payvand +2
State-space models (SSMs) have recently gained attention in deep learning for their ability to efficiently model long-range dependencies, making them promising candidates for edge-…
MINIMALIST: switched-capacitor circuits for efficient in-memory computation of gated recurrent units
Sebastian Billaudelle, Laura Kriener, Filippo Moro +2
Recurrent neural networks (RNNs) have been a long-standing candidate for processing of temporal sequence data, especially in memory-constrained systems that one may find in embedde…
DenRAM: Neuromorphic Dendritic Architecture with RRAM for Efficient Temporal Processing with Delays
Simone DAgostino, Filippo Moro, Tristan Torchet +5
An increasing number of neuroscience studies are highlighting the importance of spatial dendritic branching in pyramidal neurons in the brain for supporting non-linear computation…
Core interface optimization for multi-core neuromorphic processors
Zhe Su, Hyunjung Hwang, Tristan Torchet +1
Hardware implementations of Spiking Neural Networks (SNNs) represent a promising approach to edge-computing for applications that require low-power and low-latency, and which canno…