Intel Corporation

Raghavan Kumar

Gregory Kengho Chen

Phil Knag

Huseyin Ekin Sumbul

Ram K Krishnamurthy

Sanu K Mathew

Vikram B Suresh

Sasikanth Manipatruni

Ian A Young

Abhishek A Sharma

Amrita Mathuriya

Rafael Misoczki

Manoj R Sastry

Santosh Ghosh

Sudhir K Satpathy

Victor W Lee

Andrew H Reinders

Jack T Kavalieros

Van H Le

Uygar E Avci

Nazila Haratipour

Ravi Pillarisetty

Vivek K De

Hui Jae Yoo

Harish Krishnamurthy

Zeshan A Chishti

Jae-sun Seo

Rosario Cammarota

Huichu Liu

Xiaosen Liu

Avinash Laxmisha Varna

Thomas Chen

Abhronil Sengupta

Ram Kumar Krishnamurthy

Sachin Taneja

Arvind Singh

Deepak Vinayak Kadetotad

In one embodiment, a method comprises: combining, in a first adder circuit of a cryptographic engine, a round key with masked plaintext to generate an additively masked input; converting, in a first converter of the cryptographic engine, the additively masked input to a multiplicatively masked input; and performing, in a substitution box circuit of the cryptographic engine, a non-linear inverse operation on the multiplicatively masked input when the multiplicatively masked input is non-zero, and performing the non-linear inverse operation on a random non-zero value when the multiplicatively masked input is zero. Other embodiments are described and claimed.

Side-channel resistant multiplicatively masked encryption engine with zero-value attack detection

An apparatus is described. The apparatus includes a long short term memory (LSTM) circuit having a multiply accumulate circuit (MAC). The MAC circuit has circuitry to rely on a stored product term rather than explicitly perform a multiplication operation to determine the product term if an accumulation of differences between consecutive, preceding input values has not reached a threshold.

LSTM circuit with selective input computation

Technologies for memory and I/O efficient operations on homomorphically encrypted data are disclosed. In the illustrative embodiment, a cloud compute device is to perform operations on homomorphically encrypted data. In order to reduce memory storage space and network and I/O bandwidth, ciphertext blocks can be manipulated as data structures, allowing operands for operations on a compute engine to be created on the fly as the compute engine is performing other operations, using orders of magnitude less storage space and bandwidth.

Technologies for memory and I/O efficient operations on homomorphically encrypted data

In one example an apparatus comprises a computer readable memory, an XMSS verification manager logic to manage XMSS verification functions, a one-time signature and public key generator logic, a chain function logic to implement chain function algorithms, a low latency SHA3 hardware engine, and a register bank communicatively coupled to the XMSS verification manager logic. Other examples may be described.

Low latency post-quantum signature verification for fast secure-boot

Apparatus and method for resisting side-channel attacks on cryptographic engines are described herein. An apparatus embodiment includes a cryptographic block coupled to a non-linear low-dropout voltage regulator (NL-LDO). The NL-LDO includes a scalable power train to provide a variable load current to the cryptographic block, randomization circuitry to generate randomized values for setting a plurality of parameters, and a controller to adjust the variable load current provided to the cryptographic block based on the parameters and the current voltage of the cryptographic block. The controller to cause a decrease in the variable load current when the current voltage is above a high voltage threshold, an increase in the variable load current when the current voltage is below a low voltage threshold; and a maximization of the variable load current when the current voltage is below an undervoltage threshold. The cryptographic block may be implemented with arithmetic transformations.

Time and frequency domain side-channel leakage suppression using integrated voltage regulator cascaded with runtime crypto arithmetic transformations

In one example an apparatus comprises a computer readable memory, an XMSS operations logic to manage XMSS functions, a chain function controller to manage chain function algorithms, a secure hash algorithm-2 (SHA2) accelerator, a secure hash algorithm-3 (SHA3) accelerator, and a register bank shared between the SHA2 accelerator and the SHA3 accelerator. Other examples may be described.

Combined SHA2 and SHA3 based XMSS hardware accelerator

Various systems, devices, and methods for operating on a data sequence. A system includes a set of circuits that form an input layer to receive a data sequence; first hardware computing units to transform the data sequence, the first hardware computing units connected using a set of randomly selected weights, a first hardware computing unit to: receive an input from a second hardware computing unit, determine a weight of a connection between the first and second hardware computing units using an identifier of the second hardware computing unit and a fixed random weight generator, and operate on the input using the weight to determine a state of the first hardware computing unit; and second hardware computing units to operate on states of the first computing units to generate an output based on the data sequence.

Memoryless weight storage hardware for neural networks

In one example an apparatus comprises a computer readable memory, hash logic to generate a message hash value based on an input message, signature logic to generate a signature to be transmitted in association with the message, the signature logic to apply a hash-based signature scheme to a private key to generate the signature comprising a public key, and accelerator logic to pre-compute at least one set of inputs to the signature logic. Other examples may be described.

Accelerators for post-quantum cryptography secure hash-based signing and verification

In one example an apparatus comprises a computer-readable memory, signature logic to compute a message hash of an input message using a secure hash algorithm, process the message hash to generate an array of secret key components for the input message, apply a hash chain function to the array of secret key components to generate an array of signature components, the hash chain function comprising a series of even-index hash chains and a series of odd-index hash chains, wherein the even-index hash chains and the odd-index hash chains generate a plurality of intermediate node values and a one-time public key component between the secret key components and the signature components and store at least some of the intermediate node values in the computer-readable memory for use in one or more subsequent signature operations. Other examples may be described.

Odd index precomputation for authentication path computation

A method comprises generating, during an enrollment process conducted in a controlled environment, a dark bit mask comprising a plurality of state information values derived from a plurality of entropy sources at a plurality of operating conditions for an electronic device, and using at least a portion of the plurality of state information values to generate a set of challenge-response pairs for use in an authentication process for the electronic device.

Secure PUF-based device authentication using adversarial challenge selection

Embodiments herein describe techniques for a semiconductor device including a RRAM memory cell. The RRAM memory cell includes a FinFET transistor and a RRAM storage cell. The FinFET transistor includes a fin structure on a substrate, where the fin structure includes a channel region, a source region, and a drain region. An epitaxial layer is around the source region or the drain region. A RRAM storage stack is wrapped around a surface of the epitaxial layer. The RRAM storage stack includes a resistive switching material layer in contact and wrapped around the surface of the epitaxial layer, and a contact electrode in contact and wrapped around a surface of the resistive switching material layer. The epitaxial layer, the resistive switching material layer, and the contact electrode form a RRAM storage cell. Other embodiments may be described and/or claimed.

FinFET transistor based resistive random access memory

In one example an apparatus comprises a computer readable memory, a signature logic to generate a signature to be transmitted in association with a message, the signature logic to apply a hash-based signature scheme to the message using a private key to generate the signature comprising a public key, or a verification logic to verify a signature received in association with the message, the verification logic to apply the hash-based signature scheme to verify the signature using the public key, and an accelerator logic to apply a structured order to at least one set of inputs to the hash-based signature scheme. Other examples may be described.

Message index aware multi-hash accelerator for post quantum cryptography secure hash-based signing and verification

An apparatus is described. The apparatus includes a compute-in-memory (CIM) circuit for implementing a neural network disposed on a semiconductor chip. The CIM circuit includes a mathematical computation circuit coupled to a memory array. The memory array includes an embedded dynamic random access memory (eDRAM) memory array. Another apparatus is described. The apparatus includes a compute-in-memory (CIM) circuit for implementing a neural network disposed on a semiconductor chip. The CIM circuit includes a mathematical computation circuit coupled to a memory array. The mathematical computation circuit includes a switched capacitor circuit. The switched capacitor circuit includes a back-end-of-line (BEOL) capacitor coupled to a thin film transistor within the metal/dielectric layers of the semiconductor chip. Another apparatus is described. The apparatus includes a compute-in-memory (CIM) circuit for implementing a neural network disposed on a semiconductor chip. The CIM circuit includes a mathematical computation circuit coupled to a memory array. The mathematical computation circuit includes an accumulation circuit. The accumulation circuit includes a ferroelectric BEOL capacitor to store a value to be accumulated with other values stored by other ferroelectric BEOL capacitors.

Applications of back-end-of-line (BEOL) capacitors in compute-in-memory (CIM) circuits

A compute near memory (CNM) convolution accelerator enables a convolutional neural network (CNN) to use dedicated acceleration to achieve efficient in-place convolution operations with less impact on memory and energy consumption. A 2D convolution operation is reformulated as 1D row-wise convolution. The 1D row-wise convolution enables the CNM convolution accelerator to process input activations row-by-row, while using the weights one-by-one. Lightweight access circuits provide the ability to stream both weights and input rows as vectors to MAC units, which in turn enables modules of the CNM convolution accelerator to implement convolution for both [1&#xd7;1] and chosen [n&#xd7;n] sized filters.

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Hillsboro, OR, United States of America

Raghavan Kumar