97 citations · 160 across the 5 of their papers we have counts for
5 papers
Accurate Mapping of RNNs on Neuromorphic Hardware with Adaptive Spiking Neurons
Gauthier Boeshertz, Giacomo Indiveri, Manu Nair +1
Thanks to their parallel and sparse activity features, recurrent neural networks (RNNs) are well-suited for hardware implementation in low-power neuromorphic hardware. However, map…
Mapping high-performance RNNs to in-memory neuromorphic chips
Manu V Nair, Giacomo Indiveri
The increasing need for compact and low-power computing solutions for machine learning applications has triggered significant interest in energy-efficient neuromorphic systems. How…
An ultra-low-power sigma-delta neuron circuit
Manu V Nair, Giacomo Indiveri
Neural processing systems typically represent data using leaky integrate and fire (LIF) neuron models that generate spikes or pulse trains at a rate proportional to their input amp…
A neuromorphic systems approach to in-memory computing with non-ideal memristive devices: From mitigation to exploitation
Melika Payvand, Manu V Nair, Lorenz K. Muller +1
Memristive devices represent a promising technology for building neuromorphic electronic systems. In addition to their compactness and non-volatility features, they are characteriz…
A differential memristive synapse circuit for on-line learning in neuromorphic computing systems
Manu V Nair, Lorenz K. Muller, Giacomo Indiveri
Spike-based learning with memristive devices in neuromorphic computing architectures typically uses learning circuits that require overlapping pulses from pre- and post-synaptic no…