Search Results for author:
Found 3 papers, 0 papers with code
Analog, In-memory Compute Architectures for Artificial Intelligence
This paper presents an analysis of the fundamental limits on energy efficiency in both digital and analog in-memory computing architectures, and compares their performance to single instruction, single data (scalar) machines specifically in the context of machine inference.
Hardware-efficient on-line learning through pipelined truncated-error backpropagation in binary-state networks
In this paper, we describe a hardware-efficient on-line learning technique for feedforward multi-layer ANNs that is based on pipelined backpropagation.
Event-Driven Contrastive Divergence for Spiking Neuromorphic Systems
However the traditional RBM architecture and the commonly used training algorithm known as Contrastive Divergence (CD) are based on discrete updates and exact arithmetics which do not directly map onto a dynamical neural substrate.
