11,694 research outputs found
Recommended from our members
Silicon compilation
Silicon compilation is a term used for many different purposes. In this paper we define silicon compilation as a mapping from some higher level description into layout. We define the basic issues in structural and behavioral silicon compilation and some possible solutions to those issues. Finally, we define the concept of an intelligent silicon compiler in which the compiler evaluates the quality of the generated design and attempts to improve it if it is not satisfactory
Stepping Stones to Inductive Synthesis of Low-Level Looping Programs
Inductive program synthesis, from input/output examples, can provide an
opportunity to automatically create programs from scratch without presupposing
the algorithmic form of the solution. For induction of general programs with
loops (as opposed to loop-free programs, or synthesis for domain-specific
languages), the state of the art is at the level of introductory programming
assignments. Most problems that require algorithmic subtlety, such as fast
sorting, have remained out of reach without the benefit of significant
problem-specific background knowledge. A key challenge is to identify cues that
are available to guide search towards correct looping programs. We present
MAKESPEARE, a simple delayed-acceptance hillclimbing method that synthesizes
low-level looping programs from input/output examples. During search, delayed
acceptance bypasses small gains to identify significantly-improved stepping
stone programs that tend to generalize and enable further progress. The method
performs well on a set of established benchmarks, and succeeds on the
previously unsolved "Collatz Numbers" program synthesis problem. Additional
benchmarks include the problem of rapidly sorting integer arrays, in which we
observe the emergence of comb sort (a Shell sort variant that is empirically
fast). MAKESPEARE has also synthesized a record-setting program on one of the
puzzles from the TIS-100 assembly language programming game.Comment: AAAI 201
Programming multi-level quantum gates in disordered computing reservoirs via machine learning and TensorFlow
Novel machine learning computational tools open new perspectives for quantum
information systems. Here we adopt the open-source programming library
TensorFlow to design multi-level quantum gates including a computing reservoir
represented by a random unitary matrix. In optics, the reservoir is a
disordered medium or a multi-modal fiber. We show that trainable operators at
the input and the readout enable one to realize multi-level gates. We study
various qudit gates, including the scaling properties of the algorithms with
the size of the reservoir. Despite an initial low slop learning stage,
TensorFlow turns out to be an extremely versatile resource for designing gates
with complex media, including different models that use spatial light
modulators with quantized modulation levels.Comment: Added a new section and a new figure about implementation of the
gates by a single spatial light modulator. 9 pages and 4 figure
- …