Abstract: Provided are a processing device and a method for secure booting thereof, in which the processing device includes a security core that operates a Root of Trust and sequentially performs an integrity check on first firmware and second firmware through the Root of Trust, a main core that sequentially operates the first firmware and the second firmware, a non-volatile memory storing the first firmware and the second firmware, and a first volatile memory that loads the first firmware and the second firmware from a main core domain of the main core and operates the loaded firmware with the main core.

Abstract: A processing device comprises a first set of processors comprising a first processor and a second processor, each of which comprises at least one controllable port, a first memory operably coupled to the first set of processors, at least one forward data line configured for one-way transmission of data in a forward direction between the first set of processors, and at least one backward data line configured for one-way transmission of data in a backward direction between the first set of processors. wherein the first set of processors are operably coupled in series via the at least one forward data line and the at least one backward data line.

Abstract: A processing device comprises processors, a first memory shared by the processors, and a cache comprising a second memory comprising a plurality of memory units, each of the plurality of memory units in the second memory being associated with a respective one of a plurality of request identifiers. The cache receives a memory read request including a request identifier and a memory address from at least one of the processors, identifies an allocated memory address identifier for the memory address, accesses the first memory to read data of the memory address, obtains one or more request identifiers which requested data of the memory address from the second memory based on the allocated memory address identifier, and transmitting the data of the memory address to one or more processors which requested data of the memory address based on the one or more request identifiers.

Abstract: A neural core, a neural processing device including same and a method for lauding data of a neural processing device are provided. The neural core comprises a processing unit configured to perform operations, an L0 memory configured to store input data and an LSU configured to perform a load task and a store task of data between the processing unit and the L0 memory, wherein the LSU comprises a local memory load unit configured to transmit the input data in the L0 memory to the processing unit, and the local memory load unit comprises a target decision module configured to identify and retrieve the input data in the L0 memory, a transformation logic configured to transform the input data and thereby generate transformed data and an output FIFO configured to receive the transformed data and transmit the transformed data to the processing unit in the received order.

Abstract: A neural processing device, a processing element included therein and a method for operating various formats of the neural processing device are provided. The neural processing device includes at least one neural processor, a shared memory shared by the at least one neural processor, and a global interconnection configured to transmit data between the at least one neural processor and the shared memory, wherein each of the at least one neural processor comprises at least one processing element, each of the at least one processing element receives an input in a first format and thereby performs an operation, and receives an input in a second format that is different from the first format and thereby performs an operation if a format conversion signal is received, and the first format and the second format have a same number of bits.

Abstract: A neural processing device comprising processing circuitry are provided. A neural processing device comprises a plurality of processing engine groups; a first memory shared by the plurality of engine groups; a first interconnection configured to transmit data between the first memory and the plurality of processing engine groups. The neural processing device is configured to provide hardware resource to the plurality of processing engine groups. The at least one of the plurality of processing engine groups comprises a plurality of processing engines, each of the plurality of processing engines comprising an array of a plurality of processing elements interconnected by a mesh style network, the processing elements being reconfigurable; a second memory shared by the plurality of processing engines; and a second interconnection configured to transmit data between the second memory and the plurality of processing engines.

Abstract: A neural processor and a method for fetching instructions thereof are provided. The neural processor includes a local memory in which weights, input activations, and partial sums are stored, a processing unit configured to compute the weights, the input activations, and the partial sums, and a local memory load unit configured to load the weights, the input activations, and the partial sums from the local memory into the processing unit, wherein the local memory load unit includes an instruction fetch unit configured to fetch instructions included in a program of the local memory load unit for loading any one of the weights, the input activations, or the partial sums from the local memory, and an instruction execution unit configured to generate control signals for executing instructions fetched by the instruction fetch unit.

Abstract: Provided is a method for converting order book data into 2D data for a machine learning model, which is performed by one or more processors, in which the method includes acquiring order book data including data items for a plurality of bid prices and data items for a plurality of ask prices for a stock traded in a first stock exchange according to time from a time point in the past that is a predetermined period of time earlier, to a current time point, calculating a mid price based on a highest bid price of the plurality of bid prices and a lowest ask price of the plurality of ask prices, and generating data in tensor form having a first axis of times at certain time intervals and a second axis of prices in units of tick.

Abstract: A method for measuring performance of neural processing devices and devices for measuring performance are provided. The method for measuring performance of neural processing devices comprises receiving hardware information of a neural processing device, modeling hardware components according to the hardware information as agents, dividing a calculation task by events for the agents and modeling the calculation task, thereby generating an event model which includes nodes corresponding to the agents and edges corresponding to the events and measuring a total duration of the calculation task through simulation of the event model.

Abstract: A neural processing device and method for pruning thereof are provided. The neural processing device includes a processing unit configured to perform calculations, an L0 memory configured to store input and output data of the processing unit, wherein the input and output data include a two-dimensional weight matrix and a weight manipulator configured to receive the two-dimensional weight matrix and partition it into preset sizes to thereby generate partitioned matrices, to generate a pruning matrix by pruning the partitioned matrix, and to transmit the pruning matrix to the processing unit.

Abstract: A method for runtime integrity check, performed by a security core including one or more processors includes storing a first output value, which is generated by using a one-way encryption algorithm based on first data and a first encryption key managed by an encryption key manager accessible by the security core, in a main memory that is a volatile memory in association with the first data, generating a second output value for the first data based on the first data and the first encryption key by using the one-way encryption algorithm, and checking for possible tampering of the first data stored in the main memory by comparing the first output value with the generated second output value.

Abstract: The present disclosure discloses a processing element and a neural processing device including the processing element. The processing element includes a weight register configured to store a weight, an input activation register configured to store input activation, a flexible multiplier configured to generate result data by performing a multiplication operation of the weight and the input activation by using a first multiplier of a first precision or using both the first multiplier and a second multiplier of the first precision in response to a calculation mode signal and a saturating adder configured to generate a partial sum by using the result data.

Abstract: A neural processing device and a method for job scheduling are provided. The neural processing device configured to receive, by an address space ID (ASID) manager, first and second requests from at least one context, respectively, and determine whether ASIDs are allocated, store jobs of contexts to which the ASIDs have not been allocated from the ASID manager in entities, schedule, by a job scheduler, an execution order of the jobs stored in the entities and cause the ASID manager to allocate the ASIDs to the contexts to which the ASIDs have not been allocated among the at least one context, and sequentially receive, by a command queue, jobs of contexts to which the ASIDs have been allocated, store the jobs as standby jobs, and sequentially execute the standby jobs.

Abstract: A method for adjusting a hardware performance for high frequency trading is provided, which is performed by one or more processors, and includes receiving context information including at least one of a traffic of market data for one or more target items or a computational complexity of using a machine learning model performed based on the market data, determining whether or not to change a processing performance of the hardware based on the received context information, and changing a resource supplied to the hardware based on the determination result for changing the processing performance.

Abstract: A method for high frequency trading is provided, which is performed by one or more processors, and includes calculating a latency for a market order for each of a plurality of candidate batch sizes, selecting a batch size from among the plurality of candidate batch sizes based on the calculated latency, generating input data corresponding to the selected batch size using market data for a target item, using a machine learning model, generating prediction data for the target item at a future time point associated with the selected batch size, based on the generated input data, and generating order data for the target item based on the generated prediction data.

Abstract: A neural processing device is provided. The neural processing device comprises: a processing unit configured to receive an input activation and a weight and perform a two-dimensional matrix calculation with the input activation and the weight to generate an output activation, a first memory, and a load-store unit (LSU) configured to perform memory access operations between the first memory and a second memory. The memory access operations include a main memory access operation for a current processing operation that is performed by the processing unit, and a standby memory access operation for a standby processing operation that is performed by the processing unit after the current processing operation. A level of the first memory is equal to a level of the processing unit, and a level of the second memory is different from the level of the first memory.

Abstract: A neural processing device and a method for using shared page table thereof are provided. The neural processing device including at least one neural processor, a shared memory shared by the at least one neural processor, and a global interconnection configured to exchange data between the at least one neural processor and the shared memory, comprises at least one processing unit each of which included in each of the at least one neural processor and configured to provide logical addresses, a memory management unit configured to receive and translate the logical addresses into physical addresses, and a physical memory accessible by the physical addresses, wherein the memory management unit comprises a shared page table that has translation information between the logical addresses and the physical addresses and is shared by at least one process with each other.

Abstract: A neural processing device is provided. The neural processing device comprises: a processing unit configured to perform calculations, an L0 memory configured to receive data from the processing unit and provide data to the processing unit, and an LSU (Load/Store Unit) configured to perform load and store operations of the data, wherein the LSU comprises: a neural core load unit configured to issue a load instruction of the data, a neural core store unit configured to issue a store instruction for transmitting and storing the data, and a sync ID logic configured to provide a sync ID to the neural core load unit and the neural core store unit to thereby cause a synchronization signal to be generated for each sync ID.

Abstract: The disclosure relates to an apparatus for high frequency trading. The apparatus includes one or more memories, at least one reconfigurable processor coupled to the one or more memories, and a dedicated accelerator preconfigured for the machine learning model. The one or more processors receive market-related information from one or more market-related information servers and generates market prediction reference data based on the market-related information. The dedicated accelerator performs operations for the machine learning model with the market prediction reference data to generate future market prediction data. The at least one reconfigurable processor generates an order signal based on the future market prediction data and transmits the order signal to a target exchange server.

Abstract: A neural processing device is provided. The neural processing device comprises: an activation buffer in which first and second input activations are stored, an activation compressor configured to generate a first compressed input activation by using the first and second input activations, and a tensor unit configured to perform two-dimensional calculations using the first compressed input activation, wherein the first compressed input activation comprises first input row data comprising at least a portion of the first input activation and at least a portion of the second input activation, and first metadata corresponding to the first input row data.