Enabling Energy-Efficient Unsupervised Monocular Depth Estimation on ARMv7-Based Platforms. - Valentino Peluso; Antonio Cipolletta; Andrea Calimera; Matteo Poggi; Fabio Tosi; Stefano Mattoccia (DATE, 2019)

Integer ConvNets on Embedded CPUs: Tools and Performance Assessment on the Cortex-A Cores. - Valentino Peluso; Antonio Cipolletta; Francesco Vaiana; Andrea Calimera (ICECS, 2019)

Inference on the Edge: Performance Analysis of an Image Classification Task Using Off-The-Shelf CPUs and Open-Source ConvNets. - Valentino Peluso; Roberto Giorgio Rizzo; Antonio Cipolletta; Andrea Calimera (SNAMS, 2019)

Enabling monocular depth perception at the very edge. - Valentino Peluso; Antonio Cipolletta; Andrea Calimera; Matteo Poggi; Fabio Tosi; Filippo Aleotti; Stefano Mattoccia (CVPR Workshops, 2020)

On The Efficiency of Sparse-Tiled Tensor Graph Processing For Low Memory Usage. - Antonio Cipolletta; Andrea Calimera (DAC, 2021)

Dataflow Restructuring for Active Memory Reduction in Deep Neural Networks. - Antonio Cipolletta; Andrea Calimera (DATE, 2021)

Going Further With Winograd Convolutions: Tap-Wise Quantization for Efficient Inference on 4x4 Tiles. - Renzo Andri; Beatrice Bussolino; Antonio Cipolletta; Lukas Cavigelli; Zhe Wang 023 (MICRO, 2022)

Going Further With Winograd Convolutions: Tap-Wise Quantization for Efficient Inference on 4x4 Tile. - Renzo Andri; Beatrice Bussolino; Antonio Cipolletta; Lukas Cavigelli; Zhe Wang 023 (CoRR, 2022)

Energy-Quality Scalable Monocular Depth Estimation on Low-Power CPUs. - Antonio Cipolletta; Valentino Peluso; Andrea Calimera; Matteo Poggi; Fabio Tosi; Filippo Aleotti; Stefano Mattoccia (IEEE Internet Things J., 2022)

Monocular Depth Perception on Microcontrollers for Edge Applications. - Valentino Peluso; Antonio Cipolletta; Andrea Calimera; Matteo Poggi; Fabio Tosi; Filippo Aleotti; Stefano Mattoccia (IEEE Trans. Circuits Syst. Video Technol., 2022)

Dynamic ConvNets on Tiny Devices via Nested Sparsity. - Matteo Grimaldi; Luca Mocerino; Antonio Cipolletta; Andrea Calimera (IEEE Internet Things J., 2023)