④❞♦•q✉❡s✱❤✉❝❤❛r❞✱♥❡❜✉t✱r❡✐t③⑥❅•✐r♠♠✳❢r ❆❜str❛❝t✳ ❯s❡ ❝❛s❡ ❞✐❛❣r❛♠s ❛♣♣❡❛r ✐♥ ❡❛r•② st❡♣s ♦❢ ❛ ❯▼▲✲❜❛s❡❞ ❞❡✲ ✈❡•♦♣♠❡♥t✳ ❚❤❡② ❝❛♣t✉r❡ ✉s❡r r❡q✉✐r❡♠❡♥ts✱ str✉❝t✉r❡❞ ❜② t❤❡ ❝♦♥❝❡♣ts ♦❢ ✉s❡ ❝❛s❡s ❛♥❞ ❛❝t♦rs✳ ❆♥ ❛❞♠✐tt❡❞ ❣♦♦❞ ♠♦❞❡•✐♥❣ ♣r❛❝t✐❝❡ ✐s t♦ ❞❡✲ s✐❣♥ s✐♠♣•❡✱ ❡❛s②✲t♦✲r❡❛❞ ✉s❡ ❝❛s❡ ❞✐❛❣r❛♠s✳ ❚❤❛t ❝❛♥ ❜❡ ❛❝❤✐❡✈❡❞ ❜②✐s t❤❡ ✉s❡ ❝❛s❡ ❞✐❛❣r❛♠✱ t❤❛t ✐s •❛t❡r ✉s❡❞ ❛•• ♦✈❡r t❤❡ ♠♦❞❡•✐♥❣ ♣r♦❝❡ss✳ ❋♦r ❡①❛♠♣•❡✱ ✐♥ t❤❡ |❛t✐♦♥❛• ❯♥✐✜❡❞ Pr♦❝❡ss ❬✶✶(✱ t❤❡ ❞❡✈❡•♦♣♠❡♥t ♣r♦❝❡ss ✐s ❞r✐✈❡♥ ❜② t❤❡ ✉s❡ ❝❛s❡s✿ t❤❡ ✉s❡ ❝❛s❡s ❛r❡ ❝♦♥t✐♥✉♦✉s•② ✉s❡❞ t♦ ✐♥•❡❝t ❢✉rt❤❡r ❢✉♥❝t✐♦♥❛•✐t✐❡s ✐♥ st❛t✐❝ ♠♦❞❡•s✱ t❤❡② ❣✉✐❞❡ t❤❡ t❡st✐♥❣ ♣•❛♥✱ ❡t❝✳ ❚❤✉s ❡✈❡♥ ✐❢ t❤❡ ✉s❡ ❝❛s❡ ❞✐❛❣r❛♠ ✐s ♣r♦❜❛❜•② t❤❡ ❢✉rt❤❡st ♦♥❡ ❢r♦♠ ✐♠♣•❡♠❡♥t❛t✐♦♥✱ ✐t t❛❦❡s ❛ •❛r❣❡ ♣•❛❝❡ ✐♥ t❤❡ ❞❡s✐❣♥ ♦❢ ♠❛♥② ❛rt✐❢❛❝ts✳ ❯s❡ ❝❛s❡ ❞✐❛❣r❛♠s ♠♦❞❡• t❤❡ ❜♦✉♥❞❛r② ♦❢ ❛ s②st❡♠✱ t❤❡ ❛❝t♦rs t❤❛t ✐♥t❡r❛❝t ✇✐t❤ t❤❡ s②st❡♠✱ ❛♥❞ t❤❡ ✉s❡ ❝❛s❡s ✭♠❛✐♥ ❢✉♥❝t✐♦♥❛•✐t✐❡s ♦❢✲ ❢❡r❡❞ t♦ ❛❝t♦rs ❜② t❤❡ s②st❡♠✮✳ ❯s❡ ❝❛s❡s ❛r❡ •✐♥❦❡❞ t♦ t❤❡ ❛❝t♦rs ✐♥t❡r❛❝t✐♥❣ ✇✐t❤ t❤❡♠✱ ❛♥❞ ✉s❡ ❝❛s❡s ♠❛② ❜❡ ♦r❣❛♥✐③❡❞ ✉s✐♥❣ t❤r❡❡ t②♣❡s ♦❢ r❡•❛t✐♦♥✿ ✐♥❝•✉s✐♦♥✱ ❡①t❡♥s✐♦♥ ✭t❤❛t ❝❛♥ ❜❡ s❡❡♥ ❛s ❝♦♥❞✐t✐♦♥❛• ✐♥❝•✉s✐♦♥✮✱ ❛♥❞ ❣❡♥❡r❛•✐③❛t✐♦♥✳ ❆s ♠❡♥t✐♦♥❡❞ ✐♥ t❤❡ ❯▼▲ ✉s❡r ❣✉✐❞❡ ❬✷(✱ ✏❖r❣❛♥✐③✐♥❣ ②♦✉r ✉s❡ ❝❛s❡s ❜② ❡①✲ tr❛❝t✐♥❣ ❝♦♠♠♦♥ ❜❡❤❛✈✐♦r ✭t❤r♦✉❣❤ ✐♥❝•✉❞❡ r❡•❛t✐♦♥s❤✐♣s✮ ❛♥❞ ❞✐st✐♥❣✉✐s❤✐♥❣ ✈❛r✐❛♥ts ✭t❤r♦✉❣❤ ❡①t❡♥❞ r❡•❛t✐♦♥s❤✐♣s✮ ✐s ❛♥ ✐♠♣♦rt❛♥t ♣❛rt ♦❢ ❝r❡❛t✐♥❣ ❛ s✐♠✲ ♣•❡✱ ❜❛•❛♥❝❡❞✱ ❛♥❞ ✉♥❞❡rst❛♥❞❛❜•❡ s❡t ♦❢ ✉s❡ ❝❛s❡s ❢♦r ②♦✉r s②st❡♠✳✑ ❆ ❣♦♦❞ ♣r❛❝t✐❝❡ ❢♦r ✉s❡ ❝❛s❡ ❞✐❛❣r❛♠s ✐s t♦ ♠❛❦❡ t❤❡♠ s✐♠♣•❡ t♦ r❡❛❞ ❬✶(✱ s♦ ❛s t♦ ❝❛t❝❤ ❛t ✜rst ❣•❛♥❝❡ t❤❡ ❜♦✉♥❞❛r✐❡s ♦❢ t❤❡ s②st❡♠✱ ✐ts ♠❛✐♥ ❛❝t♦rs ❛♥❞ ♠❛✐♥ ❢✉♥❝✲ t✐♦♥❛•✐t✐❡s✳ ❚❤✉s ❛ t❛♥❣•❡❞ ✉s❡ ❝❛s❡ ❞✐❛❣r❛♠ ✭❡✳❣✳ ✇✐t❤ ♥✉♠❡r♦✉s ❛♥❞ t❛♥❣•❡❞ r❡•❛t✐♦♥s •✐♥❦✐♥❣ ✉s❡ ❝❛s❡s t♦ ❛❝t♦rs ❛♥❞ t♦ ♦t❤❡r ✉s❡ ❝❛s❡s✮ ✇✐•• r❡❣✉•❛r•② ❜r❛❦❡ t❤❡ ❞❡✈❡•♦♣♠❡♥t s✐♥❝❡ ❛ ❞❡s✐❣♥❡r✱ ❡❛❝❤ t✐♠❡ ❤❡ ♦r s❤❡ ✇✐•• ❝♦♥s✉•t ✐t✱ ✇✐•• ❤❛✈❡ ❛ ❞❡•❛② t♦ ✉♥❞❡rst❛♥❞ ✭❛❣❛✐♥✮ t❤❡ ✉s❡ ❝❛s❡ ❞✐❛❣r❛♠✳

Current processor and multiprocessor architectures are almost all based on the Von Neumann paradigm. Based on this paradigm, one can build a general-purpose computer using very few transistors, e.g., 2250 transistors in the first Intel 4004 microprocessor. In other terms, the notion that on-chip space is a scarce resource is at the root of this paradigm which trades on-chip space for program execution time. Today, technology considerably relaxed this space constraint. Still, few research works question this paradigm as the most adequate basis for high-performance computers, even though the paradigm was not initially designed to scale with technology and space.In this article, we propose a different computing model, defining both an architecture and a language, that is intrinsically designed to exploit space; we then investigate the implementation issues of a computer based on this model, and we provide simulation results for small programs and a simplified architecture as a first proof of concept. Through this model, we also want to outline that revisiting some of the principles of today's computing paradigm has the potential of overcoming major limitations of current architectures.

In Model Driven Engineering (MDE), model transformations are basic and primordial entities, thus easing their design and implementation is an important issue. A quite recently proposed way to create model transformations consists in deducing a transformation from examples of transformed models.Examples are easier to write than a transformation program and are often already available. We propose in this paper a method based on a machine learning method of the lattice domain, the Relational Concept Analysis, and an implementation of this method.