Techniques and systems for solving Boolean satisfiability (SAT) problems are described. Some embodiments solve SAT problems using efficient construction of truth tables. Some embodiments can improve performance of SAT solvers by using truth tables instead of incurring the overhead of Conjunctive Normal Form (CNF) conversion.

A method includes dividing a fraction of a floating point result into a first portion and a second portion. The method includes outputting a first normalizer result based on the first portion during to a first clock cycle. The method includes storing a first segment of the first portion during to the first clock cycle. The method includes outputting a first rounder result based on the first normalizer result during to the first clock cycle. The method includes outputting a second normalizer result based on the second portion during to a second clock cycle. The method includes outputting a second rounder result based on the second normalizer result and the first segment during to the second clock cycle.

Various embodiments of the invention implement countermeasures designed to withstand attacks by potential intruders who seek partial or full retrieval of elliptic curve secrets by using Various embodiments of the invention implement countermeasures designed to withstand attacks by potential intruders who seek partial or full retrieval of elliptic curve secrets by using known methods that exploit system vulnerabilities, including elliptic operation differentiation, dummy operation detection, lattice attacks, and first real operation detection. Various embodiments of the invention provide resistance against side-channel attacks, such as simple power analysis, caused by the detectability of scalar values from information leaked during regular operation flow that would otherwise compromise system security. In certain embodiments, system immunity is maintained by performing elliptic scalar operations that use secret-independent operation flow in a secure Elliptic Curve Cryptosystem.

An embodiment of the invention is a processor including execution circuitry to calculate, in response to a decoded instruction, a result of a complex multiplication of a first complex number and a second complex number. The calculation includes a first operation to calculate a first term of a real component of the result and a first term of the imaginary component of the result. The calculation also includes a second operation to calculate a second term of the real component of the result and a second term of the imaginary component of the result. The processor also includes a decoder, a first source register, and a second source register. The decoder is to decode an instruction to generate the decoded instruction. The first source register is to provide the first complex number and the second source register is to provide the second complex number.

A method for calculating a neuron layer of a multi-layer perceptron model that includes a permanently hardwired processor core configured in hardware for calculating a permanently predefined processing algorithm in coupled functional blocks, a neuron of a neuron layer of the perceptron model being calculated with the aid of an activation function, the activation function corresponding to a simplified sigmoid function and to a simplified tan h function, the activation function being formed by zero-point mirroring of the negative definition range of the exponential function.

An architecture and associated techniques of an apparatus for hardware accelerated machine learning are disclosed. The architecture features multiple memory banks storing tensor data. The tensor data may be concurrently fetched by a number of execution units working in parallel. Each operational unit supports an instruction set specific to certain primitive operations for machine learning. An instruction decoder is employed to decode a machine learning instruction and reveal one or more of the primitive operations to be performed by the execution units, as well as the memory addresses of the operands of the primitive operations as stored in the memory banks. The primitive operations, upon performed or executed by the execution units, may generate some output that can be saved into the memory banks. The fetching of the operands and the saving of the output may involve permutation and duplication of the data elements involved.

An architecture and associated techniques of an apparatus for hardware accelerated machine learning are disclosed. The architecture features multiple memory banks storing tensor data. The tensor data may be concurrently fetched by a number of execution units working in parallel. Each operational unit supports an instruction set specific to certain primitive operations for machine learning. An instruction decoder is employed to decode a machine learning instruction and reveal one or more of the primitive operations to be performed by the execution units, as well as the memory addresses of the operands of the primitive operations as stored in the memory banks. The primitive operations, upon performed or executed by the execution units, may generate some output that can be saved into the memory banks. The fetching of the operands and the saving of the output may involve permutation and duplication of the data elements involved.

A hardware device is provided to perform a plurality of operations to convert an input value directly from one format to another format. The hardware device is to perform the plurality of operations based on execution of an instruction. The plurality of operations includes scaling the input value to provide a scaled result and converting the scaled result from the one format to provide a converted result in the other format. The scaling and converting are to be performed as part of executing the instruction. The converted result in the other format is provided to be used in processing within the computing environment.

A hardware device is provided to perform a plurality of operations to convert an input value directly from one format to another format. The hardware device is to perform the plurality of operations based on execution of an instruction. The plurality of operations includes scaling the input value to provide a scaled result and converting the scaled result from the one format to provide a converted result in the other format. The scaling and converting are to be performed as part of executing the instruction. The converted result in the other format is provided to be used in processing within the computing environment.

Memory systems and operating method of a memory system are provided. The memory system utilized for performing a computing-in-memory (CiM) operation comprises a memory array and a processing circuit. The memory array comprises a plurality of memory cells. The processing circuit is coupled to the memory array and comprises a programming circuit and a control circuit. The programming circuit is coupled to the memory array and configured to perform a write operation for programming electrical characteristics of the memory cells. The control circuit is coupled to the programming circuit and configured to: receive a plurality of weight data corresponding to a plurality of weight values; and control the write operation performed by the programming circuit, so the electrical characteristics of the memory cells are programmed following a sequential order of the weight values.