Search Results for author:
Found 15 papers, 3 papers with code
Differentiable Transportation Pruning
Deep learning algorithms are increasingly employed at the edge.
On the visual analytic intelligence of neural networks
Visual oddity task was conceived as a universal ethnic-independent analytic intelligence test for humans.
Towards efficient end-to-end speech recognition with biologically-inspired neural networks
Automatic speech recognition (ASR) is a capability which enables a program to process human speech into a written form.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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Learning in Deep Neural Networks Using a Biologically Inspired Optimizer
Here, we propose a novel biologically inspired optimizer for artificial (ANNs) and spiking neural networks (SNNs) that incorporates key principles of synaptic integration observed in dendrites of cortical neurons: GRAPES (Group Responsibility for Adjusting the Propagation of Error Signals).
Optimality of short-term synaptic plasticity in modelling certain dynamic environments
Biological neurons and their in-silico emulations for neuromorphic artificial intelligence (AI) use extraordinarily energy-efficient mechanisms, such as spike-based communication and local synaptic plasticity.
Online Spatio-Temporal Learning in Deep Neural Networks
For shallow networks, OSTL is gradient-equivalent to BPTT enabling for the first time online training of SNNs with BPTT-equivalent gradients.
ESSOP: Efficient and Scalable Stochastic Outer Product Architecture for Deep Learning
Strategies to improve the efficiency of MVM computation in hardware have been demonstrated with minimal impact on training accuracy.
Compiling Neural Networks for a Computational Memory Accelerator
Computational memory (CM) is a promising approach for accelerating inference on neural networks (NN) by using enhanced memories that, in addition to storing data, allow computations on them.
5 Parallel Prism: A topology for pipelined implementations of convolutional neural networks using computational memory
We show that this communication fabric facilitates the pipelined execution of all state of-the-art CNNs by proving the existence of a homomorphism between one graph representation of these networks and the proposed graph topology.
Accurate deep neural network inference using computational phase-change memory
In-memory computing is a promising non-von Neumann approach where certain computational tasks are performed within memory units by exploiting the physical attributes of memory devices.
Emerging Technologies
Supervised Learning in Spiking Neural Networks with Phase-Change Memory Synapses
Combining the computational potential of supervised SNNs with the parallel compute power of computational memory, the work paves the way for next-generation of efficient brain-inspired systems.
Low-Power Neuromorphic Hardware for Signal Processing Applications
In this paper, we review some of the architectural and system level design aspects involved in developing a new class of brain-inspired information processing engines that mimic the time-based information encoding and processing aspects of the brain.
Deep learning incorporating biologically-inspired neural dynamics
Neural networks have become the key technology of artificial intelligence and have contributed to breakthroughs in several machine learning tasks, primarily owing to advances in deep learning applied to Artificial Neural Networks (ANNs).
Fatiguing STDP: Learning from Spike-Timing Codes in the Presence of Rate Codes
However, some spike-timing-related strengths of SNNs are hindered by the sensitivity of spike-timing-dependent plasticity (STDP) rules to input spike rates, as fine temporal correlations may be obstructed by coarser correlations between firing rates.
Mixed-Precision In-Memory Computing
As CMOS scaling reaches its technological limits, a radical departure from traditional von Neumann systems, which involve separate processing and memory units, is needed in order to significantly extend the performance of today's computers.
Emerging Technologies
