Valorization of traditional Italian walnut (Juglans regia L.) production: genetic, nutritional and sensory characterization of locally grown varieties in the Trentino region

15openYesJuglans regia (L.) is cultivated worldwide for its nutrient-rich nuts. In Italy, despite the growing demand, walnut cultivation has gone through a strong decline in recent decades, which led to Italy being among the top five net importing countries. To promote the development of local high-quality Italian walnut production, we devised a multidisciplinary project to highlight the distinctive traits of three varieties grown in the mountainous region Trentino (northeast of Italy): the heirloom ‘Bleggiana’, a second local accession called local Franquette and the French cultivar ‘Lara’, recently introduced in the local production to increase yield. The genetic characterization confirmed the uniqueness of ‘Bleggiana’ and revealed local Franquette as a newly described autochthonous variety, thus named ‘Blegette’. The metabolic profiles highlighted a valuable nutritional composition of the local varieties, richer in polyphenols and with a lower ω-6/ω-3 ratio than the commercial ‘Lara’. ‘Blegette’ obtained the highest preference scores from consumers for both the visual aspect and tasting; however, the volatile organic compound profiles did not discriminate among the characterized cultivars. The described local varieties represent an interesting reservoir of walnut genetic diversity and quality properties, which deserve future investigation on agronomically useful traits (e.g., local adaptation and water usage) for a high-quality and sustainable production.Di Pierro, Erica A.; Franceschi, Pietro; Endrizzi, Isabella; Farneti, Brian; Poles, Lara; Masuero, Domenico; Khomenko, Iuliia; Trenti, Francesco; Marrano, Annarita; Vrhovsek, Urska; Gasperi, Flavia; Biasioli, Franco; Guella, Graziano; Bianco, Luca; Troggio, MichelaDi Pierro, E.A.; Franceschi, P.; Endrizzi, I.; Farneti, B.; Poles, L.; Masuero, D.; Khomenko, I.; Trenti, F.; Marrano, A.; Vrhovsek, U.; Gasperi, F.; Biasioli, F.; Guella, G.; Bianco, L.; Troggio, M

TEMET: Truncated REconfigurable Multiplier with Error Tuning

Approximate computing is a well-established technique to mitigate power consumption in error-tolerant domains such as image processing and machine learning. When paired with reconfigurable hardware, it enables dynamic adaptability to each specific task with improved power-accuracy trade-offs. In this work, we present a design methodology to enhance the energy and error metrics of a signed multiplier. This novel approach reduces the approximation error by leveraging a statistic-based truncation strategy. Our multiplier features 256 dynamically configurable approximation levels and run-time selection of the result precision. Our technique improves the mean-relative error by up to 34% compared to the zero truncation mechanism. Compared with an exact design, we achieve a maximum of 60.1% power saving for a PSNR of 10.3dB on a 5x5 Sobel filter. Moreover, we reduce the computation energy of LeNet by 31.5%, retaining 89.4% of the original accuracy on FashionMNIST