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1470 results about "Memory interface" patented technology

Microprocessors

A processor (100) is provided that is a programmable fixed point digital signal processor (DSP) with variable instruction length, offering both high code density and easy programming. Architecture and instruction set are optimized for low power consumption and high efficiency execution of DSP algorithms, such as for wireless telephones, as well as pure control tasks. The processor includes an instruction buffer unit (106), a program flow control unit (108), an address / data flow unit (110), a data computation unit (112), and multiple interconnecting busses. Dual multiply-accumulate blocks improve processing performance. A memory interface unit (104) provides parallel access to data and instruction memories. The instruction buffer is operable to buffer single and compound instructions pending execution thereof. A decode mechanism is configured to decode instructions from the instruction buffer. The use of compound instructions enables effective use of the bandwidth available within the processor. A soft dual memory instruction can be compiled from separate first and second programmed memory instructions. Instructions can be conditionally executed or repeatedly executed. Bit field processing and various addressing modes, such as circular buffer addressing, further support execution of DSP algorithms. The processor includes a multistage execution pipeline with pipeline protection features. Various functional modules can be separately powered down to conserve power. The processor includes emulation and code debugging facilities with support for cache analysis.
Owner:TEXAS INSTR INC

Smart memory based synchronization controller for a multi-threaded multiprocessor SoC

A memory interface for use with a multiprocess memory system having a gating memory, the gating memory associating one or more memory access methods with each of a plurality of memory locations of the memory system wherein the gating memory returns a particular one access method for a particular one memory location responsive to a memory access instruction relating to the particular one memory location, the interface including: a request storage for storing a plurality of concurrent memory access instructions for one or more of the particular memory locations, each the memory access instruction issued from an associated independent thread context; an arbiter, coupled to the request storage, for selecting a particular one of the memory access instructions to apply to the gating memory; and a controller, coupled to the request storage and to the arbiter, for: storing the plurality of memory access instructions in the request storage; initiating application of the particular one memory access instruction selected by the arbiter to the gating memory; receiving the particular one access method associated with the particular one memory access method from the gating memory; and initiating a communication of the particular access method to the thread context associated with the particular one access instruction.
Owner:MIPS TECH INC

Hardware architecture of binary weight convolution neural network accelerator and calculation process thereof

The invention discloses the hardware architecture of a binary weight convolution neural network accelerator and a calculation process thereof. The hardware architecture comprises three double-ended on-chip static random access memories which are used for buffering the binary weight of input neurons and a convolution layer, four convolution processing units capable of controlling calculation parts to complete major convolution calculation operation according to the calculation process, a feature map accumulation unit and a convolutional accumulation array. The feature map accumulation unit and the convolutional accumulation array are used for further processing the operation result of the convolution processing units to acquire a final correct output neuron value. The entire design exchanges data with an off-chip memory via a dynamic random access memory interface. In addition to the hardware architecture, the invention further provides the detailed calculation process which optimizes the hardware architecture and uses four lines of input feature map as a complete calculation unit. According to the invention, input data are reused to the greatest extent; the access of the off-chip memory is eliminated as much as possible; the power consumption of the deep binary convolution neural network calculation can be effectively reduced; a deep network is supported; and the scheme provided by the invention is a reasonable scheme which can be applied to an embedded system of visual application.
Owner:南京风兴科技有限公司
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