83 citations · 180 across the 15 of their papers we have counts for
5 papers · 1 filter
Implementing Binarized Neural Networks with Magnetoresistive RAM without Error Correction
Tifenn Hirtzlin, Bogdan Penkovsky, Jacques-Olivier Klein +5
One of the most exciting applications of Spin Torque Magnetoresistive Random Access Memory (ST-MRAM) is the in-memory implementation of deep neural networks, which could allow impr…
Digital Biologically Plausible Implementation of Binarized Neural Networks with Differential Hafnium Oxide Resistive Memory Arrays
Tifenn Hirtzlin, Marc Bocquet, Bogdan Penkovsky +5
The brain performs intelligent tasks with extremely low energy consumption. This work takes inspiration from two strategies used by the brain to achieve this energy efficiency: the…
Stochastic Computing for Hardware Implementation of Binarized Neural Networks
Tifenn Hirtzlin, Bogdan Penkovsky, Marc Bocquet +3
Binarized Neural Networks, a recently discovered class of neural networks with minimal memory requirements and no reliance on multiplication, are a fantastic opportunity for the re…
Outstanding Bit Error Tolerance of Resistive RAM-Based Binarized Neural Networks
Tifenn Hirtzlin, Marc Bocquet, Jacques-Olivier Klein +4
Resistive random access memories (RRAM) are novel nonvolatile memory technologies, which can be embedded at the core of CMOS, and which could be ideal for the in-memory implementat…
In-Memory and Error-Immune Differential RRAM Implementation of Binarized Deep Neural Networks
Marc Bocquet, Tifenn Hirztlin, Jacques-Olivier Klein +4
RRAM-based in-Memory Computing is an exciting road for implementing highly energy efficient neural networks. This vision is however challenged by RRAM variability, as the efficient…