Arithmetic precision scaling is mandatory to deploy Convolutional Neural Networks (CNNs) on resource-constrained devices such as microcontrollers (MCUs), and quantization via fixed-point or binarization are the most adopted techniques today. Despite being born by the same concept of bit-width lowering, these two strategies differ substantially each other, and hence are often conceived and implemented separately. However, their joint integration is feasible and, if properly implemented, can bring to large savings and high processing efficiency. This work elaborates on this aspect introducing a boosted collaborative mechanism that pushes CNNs towards higher performance and more predictive capability. Referred as BC-Net, the proposed solution consists of a self-adaptive conditional scheme where a lightweight binary net and an 8-bit quantized net are trained to cooperate dynamically. Experiments conducted on four different CNN benchmarks deployed on off-the-shelf boards powered with the MCUs of the Cortex-M family by ARM show that BC-Nets outperform classical quantization and binarization when applied as separate techniques (up to 81.49% speed-up and up to 3.8% of accuracy improvement). The comparative analysis with a previously proposed cooperative method also demonstrates BC-Nets achieve substantial savings in terms of both performance (+19%) and accuracy (+3.45%).
Fast and Accurate Inference on Microcontrollers with Boosted Cooperative Convolutional Neural Networks (BC-Net) / Mocerino, L.; Calimera, A.. - In: IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS. I, REGULAR PAPERS. - ISSN 1549-8328. - 68:1(2021), pp. 77-88. [10.1109/TCSI.2020.3039116]
Fast and Accurate Inference on Microcontrollers with Boosted Cooperative Convolutional Neural Networks (BC-Net)
Mocerino L.;Calimera A.
2021
Abstract
Arithmetic precision scaling is mandatory to deploy Convolutional Neural Networks (CNNs) on resource-constrained devices such as microcontrollers (MCUs), and quantization via fixed-point or binarization are the most adopted techniques today. Despite being born by the same concept of bit-width lowering, these two strategies differ substantially each other, and hence are often conceived and implemented separately. However, their joint integration is feasible and, if properly implemented, can bring to large savings and high processing efficiency. This work elaborates on this aspect introducing a boosted collaborative mechanism that pushes CNNs towards higher performance and more predictive capability. Referred as BC-Net, the proposed solution consists of a self-adaptive conditional scheme where a lightweight binary net and an 8-bit quantized net are trained to cooperate dynamically. Experiments conducted on four different CNN benchmarks deployed on off-the-shelf boards powered with the MCUs of the Cortex-M family by ARM show that BC-Nets outperform classical quantization and binarization when applied as separate techniques (up to 81.49% speed-up and up to 3.8% of accuracy improvement). The comparative analysis with a previously proposed cooperative method also demonstrates BC-Nets achieve substantial savings in terms of both performance (+19%) and accuracy (+3.45%).File | Dimensione | Formato | |
---|---|---|---|
BCNet_TCASI_final_sub.pdf
accesso aperto
Tipologia:
2. Post-print / Author's Accepted Manuscript
Licenza:
Pubblico - Tutti i diritti riservati
Dimensione
1.98 MB
Formato
Adobe PDF
|
1.98 MB | Adobe PDF | Visualizza/Apri |
09275360.pdf
accesso riservato
Tipologia:
2a Post-print versione editoriale / Version of Record
Licenza:
Non Pubblico - Accesso privato/ristretto
Dimensione
1.56 MB
Formato
Adobe PDF
|
1.56 MB | Adobe PDF | Visualizza/Apri Richiedi una copia |
Pubblicazioni consigliate
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
https://hdl.handle.net/11583/2903752