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Neuro-Inspired Energy-Efficient Computing Platforms

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Neuro-Inspired Energy-Efficient Computing Platforms Neuro-Inspired Energy-Efficient Computing Platforms THÈSE No 1234 (2016) PRÉSENTÉE LE 4 JANVIER 2017 À L’Université de Lille 1 Ecole Doctorale Sciences pour L’Ingénieur POUR L’OBTENTION DU GRADE DE DOCTEUR ÈS SCIENCES PAR Matteo CAUSO acceptée sur proposition du jury : Edith Beigné, CEA-LETI rapporteur Marian Verhelst, KU Leuven rapporteur Lirida Alves de Barros Naviner, Telecom ParisTech examinateur Laurent Clavier, Université Lille 1 président Antoine Frappé, ISEN-IEMN Lille examinateur Andreia Cathelin, STMicroelectronics co-encadrant et tuteur industriel Jan M. Rabaey, UC Berkeley co-encadrant scientifique Andreas Kaiser, ISEN-IEMN Lille directeur de thèse Pascal Yim, EC Lille invité Lille, France 2016 To Nonna Tetta A: "Nonna Anna, chi è quella vecchietta nella foto con papà?" B: "Quella è la mamma mia, la nonna Tetta, la nonna di papà! Era vecchietta, ma ne faceva di cose! La nonna Tetta andava a cavallo, e ha fatto pure tante gare! Gestiva il calcio a Casarano e la sera andava in discoteca, ma alla fine si è sposata! Ha lavorato a lungo in ferrovia, poi ha aperto un'azienda. Ma non una sola, almeno due. Pensa te che tutta stanca, si svegliava alle 5 per fare pasticciotti. O almeno, lei diceva così… I pasticciotti, si! Ma faceva tante altre cose. E che fatica impastare e guidare su e giù per tutta la Puglia. É andata a Milano, la nonna Tetta, e per due volte! Prima ha fatto un corso di chimica, poi ha parlato l'inglese a l'Expo. Si, la nonna Tetta conosceva bene l'inglese! Lo ha imparato quando è stata in Irlanda, Inghilterra, America e in Australia. Là si è divertita tanto, dovevi vedere che sorriso che aveva! Così contenta l'ho vista tante altre volte, per la verità! Per esempio quando è diventata ragioniera, ingegnera o economista. Voleva pure fare l'avvocato! Per un bel po’ ha vissuto a Torino. Là la conoscevano tutti, e pure là le volevano tanto bene. Poi è partita per la Spagna ed ora parla lo spagnolo. Si, ma non solo! È emigrata in Francia per fare cose strane, e ha dovuto imparare anche il francese, altrimenti nessuno la capiva! Quando la domenica usciva da Notre Dame, dove si è cresimata, dopo la messa andava a comprare sempre il suo dolce preferito e un sablé semplice semplice. Pensa te quanto era particolare la nonna Tetta: ha chiamato la sua pasticceria proprio Sablé un nome semplice semplice. Là dentro faceva magie ad opera d'arte: panettoni, uova di Pasqua giganti, paste di mandorla e persino torte straniere, Cubane! Ma il giovedì non la tenevi... Lei doveva andare a pesca! Sì, perché lei si divertiva a pescare. Poi il pesce lo vendeva congelato. Dovevi vederla con l'uniforme, tutta bella e organizzata nel negozio tutto nuovo, con tante idee e tanti colori studiati. Ogni volta che andavamo ci offriva caffè e dolcetti da provare. Come cucinava bene, la nonna Tetta! Faceva la salsa e il pane l'estate in campagna e per ferragosto organizzava una grande festa. C'era sempre tanto da festeggiare! Aveva appena finito casa e si poteva sposare. Si, la nonna Tetta costruiva le case. Dovevi vederla col camioncino blu o con la Passat verde quante feste ti faceva quando la incrociavi per strada. Si commoveva facilmente la nonna Tetta. Ma che vuoi, era appena diventata mamma tante volte. Pensa te, una volta ha fatto tre figli in una volta! Quanti sacrifici che ha dovuto fare, quanto è stato difficile, ma alla fine ha riempito di gioia tutta la casa e che da farsi! Aveva appena fatto gli esami di maturità, quelli finali delle medie e stava studiando il latino. Aveva lavorato per tanti anni per le assicurazioni, era estetista ma le piaceva tanto danzare a tutti i livelli e tanti tipi di ballo. Alle feste non la fermavi, balli di gruppo e valzer erano la sua specialità. Però la sera si addormentava dappertutto, era stanca la nonna Tetta! La mattina in ufficio, toccava a lei! Tutti i giorni andava a Maglie e al Comune. Piaceva tanto il caffè alla nonna Tetta e fumava, pure troppo! La nonna Tetta era il Natale da lei, Padre Pio alla TV e il suo compleanno festeggiato il 12 giugno." A: "Nonna Anna, sarò pure piccolo ma non mi prendi in giro: non ha senso quello che hai detto! Ha detto la mamma che la nonna Tetta era Amore, è questo che vuoi dire?! Ho capito... È l'Amore di quando papà vi abbraccia tutti dopo tanto tempo che non vi vede. Questo è la nonna Tetta, grazie nonna Anna!" Acknowledgements Firstly, I want to thank my academic advisors Prof. Jan Rabaey, UC Berkeley, Prof. Andreas Kaiser and Prof. Antoine Frappé, ISEN-Lille and my industrial supervisor HDR Andreia Cathelin, STMicroelectronics Crolles. It has been an honor to be part of their team and to actively contribute to the success of this international collaboration. In fact, every productive and stimulating experience and even the difficulty, such a big project involved, finally built an objective- oriented, fast-failure, and extremely open-minded and dynamic research culture of mine. Through the challenges they provided me with, I matured both personally and professionally. Furthermore, the necessity to be integrated in different teams in Berkeley, California, in Lille and in Crolles, France contributed in reinforcing my educational path already oriented to the internationalization and enriched by diverse cultural backgrounds. My time at Berkeley, Lille and Grenoble/Crolles was made enjoyable in large part due to the many friends that became a part of my life. I am grateful to Antonio Puglielli, Simone Benatti, Abbas Rahimi, Lorenzo Iotti, Ali Moin, Katerina Papadopoulou, Olivia Nolan, Candy Corpus, Brian Zimmer, James Dunn, Brian Richards, Sameet Ramakrishnan, Krishna Settaluri, Bo Zhao, Stevo Bailey, Leslie Nishiyama; Cristian Marin, Ilias Sourikopoulos, Florence Alberti; Martin Cochet, Artiom Tchouprina, Etienne Marecal, Nicolas Le Dortz, Roberto Guizzetti, Dimitri Gaughet, Abhirup Lahiri, Carlo Tinella, Emmanuel Rouat, Pascal Urard, Philip Cathelin, Gabriel Mugny, Reda Kasri, Hanae Zegmout, Hani Malloug, Hani Sherry, Julien Kieffer, Florian Voineau, Pascale Maillet-Contoz. Lastly, I would like to thank my family for all their love and encouragement in support of all my pursuits. Most of all, my loving, supportive, encouraging, and patient girlfriend Selina. The joy and enthusiasm she has for her research was contagious and motivational for me, especially during tough times in the Ph.D. pursuit. Grenoble, January 4, 2017 ii Abstract Big Data highlights all the flaws of the conventional computing paradigm. Neuro-Inspired computing and other data-centric paradigms rather address Big Data to as resources to progress. In this dissertation, we adopt Hierarchical Temporal Memory (HTM) principles and theory as neuroscientific references and we elaborate on how Bayesian Machine Learning (BML) leads apparently totally different Neuro-Inspired approaches to unify and meet our main objectives: (i) simplifying and enhancing BML algorithms and (ii) approaching Neuro-Inspired computing with an Ultra-Low- Power prospective. In this way, we aim to bring intelligence close to data sources and to popularize BML over strictly constrained electronics such as portable, wearable and implantable devices. Nevertheless, BML algorithms demand for optimizations. In fact, their naïve HW implementation results neither effective nor feasible because of the required memory, computing power and overall complexity. We propose a less complex on-line, distributed nonparametric algorithm and show better results with respect to the state-of-the-art solutions. In fact, we gain two orders of magnitude in complexity reduction with only algorithm level considerations and manipulations. A further order of magnitude in complexity reduction results through traditional HW optimization techniques. In particular, we conceive a proof-of-concept on a FPGA platform for real-time stream analytics. Finally, we demonstrate we are able to summarize the ultimate findings in Machine Learning into a generally valid algorithm that can be implemented in HW and optimized for strictly constrained applications. Keywords Bayesian Machine Learning; Variational Bayesian Inference; Expectation-Maximization; On-line learning; Learning-On-a-Chip; Distributed Machine Learning; Message passing; Loopy Belief Propagation; Probabilistic Graphical Model; FPGA; Deep Learning; Neuromorphic Engineering; Probability Theory; Hierarchical Temporal Memory; Ultra-Low-Power; Optimizations. iii Résumé Les Big Data mettent en évidence tous les défauts du paradigme de l'informatique classique. Au contraire, le Neuro-Inspiré traite les Big Data comme ressources pour progresser. Dans cette thèse, nous adoptons les principes de Hierarchical Temporal Memory (HTM) comme références neuroscientifiques et nous élaborons sur la façon dont le Bayesian Machine Learning (BML) mène les approches dans le Neuro-Inspiré à s’unifier et à atteindre nos objectives: (i) la simplification et l'amélioration des algorithmes de BML et (ii) l'approche au Neuro-Inspiré avec une prospective Ultra-Low-Power. Donc, nous nous efforçons d'apporter le traitement intelligent proche aux sources de données et de populariser le BML sur l'électronique strictement limitées tels que les appareils portables, mettable et implantables. Cependant, les algorithmes de BML ont besoin d’être optimisés. En fait, leur mise en œuvre en HW est ni efficaces, ni réalisables en raison de la mémoire, la puissance de calcul requises. Nous proposons un algorithme moins complexe, en ligne, distribué et non paramétrique et montrons de meilleurs résultats par rapport aux solutions de l’état de l’art. En fait, nous gagnons deux ordres de grandeur de réduction en complexité au niveau algorithmique et un autre ordre de grandeur grâce à des techniques traditionnelles d'optimisation HW. En particulier, nous concevons une preuve de concept sur une plateforme FPGA pour l'analyse en temps réel d’un flux de données. Enfin, nous démontrons d’être en mesure de résumer les ultimes découvertes du domaine du BML sur un algorithme généralement valide qui peut être mis en œuvre en HW et optimisé pour des applications avec des ressources limitées.
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