42 citations · 63 across the 5 of their papers we have counts for
6 papers · 1 filter
Improving the Accuracy of Analog-Based In-Memory Computing Accelerators Post-Training
Corey Lammie, Athanasios Vasilopoulos, Julian Büchel +4
Analog-Based In-Memory Computing (AIMC) inference accelerators can be used to efficiently execute Deep Neural Network (DNN) inference workloads. However, to mitigate accuracy losse…
LionHeart: A Layer-based Mapping Framework for Heterogeneous Systems with Analog In-Memory Computing Tiles
Corey Lammie, Yuxuan Wang, Flavio Ponzina +7
When arranged in a crossbar configuration, resistive memory devices can be used to execute Matrix-Vector Multiplications (MVMs), the most dominant operation of many Machine Learnin…
Using the IBM Analog In-Memory Hardware Acceleration Kit for Neural Network Training and Inference
Manuel Le Gallo, Corey Lammie, Julian Buechel +8
Analog In-Memory Computing (AIMC) is a promising approach to reduce the latency and energy consumption of Deep Neural Network (DNN) inference and training. However, the noisy and n…
Design Space Exploration of Dense and Sparse Mapping Schemes for RRAM Architectures
Corey Lammie, Jason K. Eshraghian, Chenqi Li +4
The impact of device and circuit-level effects in mixed-signal Resistive Random Access Memory (RRAM) accelerators typically manifest as performance degradation of Deep Learning (DL…
Memristive Stochastic Computing for Deep Learning Parameter Optimization
Corey Lammie, Jason K. Eshraghian, Wei D. Lu +1
Stochastic Computing (SC) is a computing paradigm that allows for the low-cost and low-power computation of various arithmetic operations using stochastic bit streams and digital l…
Variation-aware Binarized Memristive Networks
Corey Lammie, Olga Krestinskaya, Alex James +1
The quantization of weights to binary states in Deep Neural Networks (DNNs) can replace resource-hungry multiply accumulate operations with simple accumulations. Such Binarized Neu…