Abstract: A method for synthesising polyepichlorohydrin including: a) reacting epichlorohydrin with boron trifluoroetherate in the presence of a polymerisation initiator; b) adding a good solvent for epichlorohydrin to the reaction product obtained in step a); c) adding epichlorohydrin to the reaction product obtained in step b).

Abstract: In an example, an apparatus comprises a plurality of execution units comprising and logic, at least partially including hardware logic, to traverse a solution space, score a plurality of solutions to a scheduling deep learning network execution, and select a preferred solution from the plurality of solutions to implement the deep learning network. Other embodiments are also disclosed and claimed.

Abstract: A method for synthesising polyepichlorohydrin includes: a) reacting epichlorohydrin with boron trifluoroetherate in the presence of a solvent; b) adding epichlorohydrin to the reaction product obtained in step a); c) hydrolysing the product obtained in step b).

Abstract: In an example, an apparatus comprises a plurality of execution units comprising and logic, at least partially including hardware logic, to traverse a solution space, score a plurality of solutions to a scheduling deep learning network execution, and select a preferred solution from the plurality of solutions to implement the deep learning network. Other embodiments are also disclosed and claimed.

Abstract: In an example, an apparatus comprises at least one execution platform; and logic, at least partially including hardware logic, to receive a trained neural network model in a model optimizer and convert the trained neural network model to an optimized model comprising parameters that are fit to the at least one execution platform. Other embodiments are also disclosed and claimed.

Abstract: In an example, an apparatus comprises a plurality of execution units comprising and logic, at least partially including hardware logic, to traverse a solution space, score a plurality of solutions to a scheduling deep learning network execution, and select a preferred solution from the plurality of solutions to implement the deep learning network. Other embodiments are also disclosed and claimed.

Abstract: In an example, an apparatus comprises a plurality of execution units and logic, at least partially including hardware logic, to gate at least one of a multiply unit or an accumulate unit in response to an input of value zero. Other embodiments are also disclosed and claimed.

Abstract: A selection process is iterative and includes an initialization associating with a so-called current molecule a value of a predetermined molecule descriptor associated with the target molecular structure, and during each iteration of the selection process, the process includes evaluating, for each molecule of a database including a plurality of molecules each associated with a value of the descriptor, a so-called overall similarity measure between the value of the descriptor associated with the molecule and the value of the descriptor associated with the current molecule; selecting molecules from the database having an overall similarity measure greater than a predetermined threshold, the selected molecules being added to the reference subset; and updating the value of the descriptor associated with the current molecule from the values of the descriptors associated with at least some of the molecules belonging to the reference subset.

Abstract: In an example, an apparatus comprises a plurality of execution units comprising and logic, at least partially including hardware logic, to traverse a solution space, score a plurality of solutions to a scheduling deep learning network execution, and select a preferred solution from the plurality of solutions to implement the deep learning network. Other embodiments are also disclosed and claimed.

Abstract: In an example, an apparatus comprises a plurality of execution units; and logic, at least partially including hardware logic, to determine a sub-graph of a network that can be executed in a frequency domain and apply computations in the sub-graph in the frequency domain. Other embodiments are also disclosed and claimed.

Abstract: In an example, an apparatus comprises a plurality of execution units; and logic, at least partially including hardware logic, to determine a sub-graph of a network that can be executed in a frequency domain and apply computations in the sub-graph in the frequency domain. Other embodiments are also disclosed and claimed.

Abstract: In an example, an apparatus comprises a plurality of execution units; and logic, at least partially including hardware logic, to determine a sub-graph of a network that can be executed in a frequency domain and apply computations in the sub-graph in the frequency domain. Other embodiments are also disclosed and claimed.

Abstract: In an example, an apparatus comprises a plurality of execution units and logic, at least partially including hardware logic, to gate at least one of a multiply unit or an accumulate unit in response to an input of value zero. Other embodiments are also disclosed and claimed.

Abstract: In an example, an apparatus comprises a plurality of execution units; and logic, at least partially including hardware logic, to determine a sub-graph of a network that can be executed in a frequency domain and apply computations in the sub-graph in the frequency domain. Other embodiments are also disclosed and claimed.

Abstract: A process for manufacturing a composite part includes introducing an adhesion promoter into the pores of a fibrous preform formed by threads covered with a coating having —OH groups on its surface, the adhesion promoter including an electron-withdrawing group G1 that is reactive according to a reaction of substitution or of nucleophilic addition with the —OH groups, and a reactive group G2; grafting the adhesion promoter to the surface of the coating by a reaction of substitution or nucleophilic addition of the —OH groups on the group G1; introducing a ceramic precursor resin into the pores of the fibrous preform; polymerizing the resin introduced and bonding the grafted adhesion promoter to the resin by chemical reaction between these two compounds at the level of the group G2, and forming a ceramic matrix phase in the pores of the fibrous preform by pyrolysis of the polymerized resin.

Abstract: In an example, an apparatus comprises a plurality of execution units and logic, at least partially including hardware logic, to gate at least one of a multiply unit or an accumulate unit in response to an input of value zero. Other embodiments are also disclosed and claimed.

Abstract: In an example, an apparatus comprises a plurality of execution units and logic, at least partially including hardware logic, to gate at least one of a multiply unit or an accumulate unit in response to an input of value zero. Other embodiments are also disclosed and claimed.

Abstract: In an example, an apparatus comprises a plurality of execution units; and logic, at least partially including hardware logic, to determine a sub-graph of a network that can be executed in a frequency domain and apply computations in the sub-graph in the frequency domain. Other embodiments are also disclosed and claimed.

Abstract: In an example, an apparatus comprises a plurality of execution units; and logic, at least partially including hardware logic, to determine a sub-graph of a network that can be executed in a frequency domain and apply computations in the sub-graph in the frequency domain. Other embodiments are also disclosed and claimed.

Abstract: A process for manufacturing a composite part includes introducing an adhesion promoter into the pores of a fibrous preform formed by threads covered with a coating having —OH groups on its surface, the adhesion promoter including an electron-withdrawing group G1 that is reactive according to a reaction of substitution or of nucleophilic addition with the —OH groups, and a reactive group G2; grafting the adhesion promoter to the surface of the coating by a reaction of substitution or nucleophilic addition of the —OH groups on the group G1; introducing a ceramic precursor resin into the pores of the fibrous preform; polymerizing the resin introduced and bonding the grafted adhesion promoter to the resin by chemical reaction between these two compounds at the level of the group G2, and forming a ceramic matrix phase in the pores of the fibrous preform by pyrolysis of the polymerized resin.