Abstract: Methods and apparatus for configuring a front end to process multiple sectors with multiple radio frequency frames. In an exemplary embodiment, a method includes decoding instructions included in a job description list, and configuring one or more processing functions of a transceiver to process a radio signal associated with a selected sector based on the decoded instructions. The configuration of the processing functions is synchronized according to time control instructions included in the job description list.

Abstract: A new approach is proposed to support efficient convolution for deep learning by vectorizing multi-dimensional input data for multi-dimensional fast Fourier transform (FFT) and direct memory access (DMA) for data transfer. Specifically, a deep learning processor (DLP) includes a plurality of tensor engines each configured to perform convolution operations by applying one or more kernels on multi-dimensional input data for pattern recognition and classification based on a neural network, wherein each tensor engine includes, among other components, one or more vector processing engines each configured to vectorize the multi-dimensional input data at each layer of the neural network to generate a plurality of vectors and to perform multi-dimensional FFT on the generated vectors and/or the kernels to create output for the convolution operations. Each tensor engine further includes a data engine configured to prefetch the multi-dimensional data and/or the kernels to both on-chip and external memories via DMA.

Abstract: Methods and apparatus for configuring a front end to process multiple sectors with multiple radio frequency frames. In an exemplary embodiment, a method includes decoding instructions included in a job description list, and configuring one or more processing functions of a transceiver to process a radio signal associated with a selected sector based on the decoded instructions. The configuration of the processing functions is synchronized according to time control instructions included in the job description list.

Abstract: Methods and apparatus for configuring a front end to process multiple sectors with multiple radio frequency frames. In an exemplary embodiment, a method includes decoding instructions included in a job description list, and configuring one or more processing functions of a transceiver to process a radio signal associated with a selected sector based on the decoded instructions. The configuration of the processing functions is synchronized according to time control instructions included in the job description list.

Abstract: A new approach is proposed to support efficient convolution for deep learning by vectorizing multi-dimensional input data for multi-dimensional fast Fourier transform (FFT) and direct memory access (DMA) for data transfer. Specifically, a deep learning processor (DLP) includes a plurality of tensor engines each configured to perform convolution operations by applying one or more kernels on multi-dimensional input data for pattern recognition and classification based on a neural network, wherein each tensor engine includes, among other components, one or more vector processing engines each configured to vectorize the multi-dimensional input data at each layer of the neural network to generate a plurality of vectors and to perform multi-dimensional FFT on the generated vectors and/or the kernels to create output for the convolution operations. Each tensor engine further includes a data engine configured to prefetch the multi-dimensional data and/or the kernels to both on-chip and external memories via DMA.

Abstract: A new approach is proposed to support efficient convolution for deep learning by vectorizing multi-dimensional input data for multi-dimensional fast Fourier transform (FFT) and direct memory access (DMA) for data transfer. Specifically, a deep learning processor (DLP) includes a plurality of tensor engines each configured to perform convolution operations by applying one or more kernels on multi-dimensional input data for pattern recognition and classification based on a neural network, wherein each tensor engine includes, among other components, one or more vector processing engines each configured to vectorize the multi-dimensional input data at each layer of the neural network to generate a plurality of vectors and to perform multi-dimensional FFT on the generated vectors and/or the kernels to create output for the convolution operations. Each tensor engine further includes a data engine configured to prefetch the multi-dimensional data and/or the kernels to both on-chip and external memories via DMA.

Abstract: Methods and apparatus for providing an FFT engine using a reconfigurable single delay feedback architecture. In one aspect, an apparatus includes a radix-2 (R2) single delay feedback (SDF) stage that generates a radix-2 output and a radix-3 (R3) SDF stage that generates a radix-3 output. The apparatus also includes one or more radix-2 squared (R2^2) SDF stages that generate a radix-4 output. The apparatus further includes a controller that configures a sequence of radix stages selected from the R2, R3, and R2^2 stages based on an FFT point size to form an FFT engine. The FFT engine receives input samples at a first stage of the sequence and generate an FFT output result that is output from a last stage of the sequence. The sequence includes no more than one R3 stage.

Abstract: A process capable of employing compression and decompression mechanism to receive and decode soft information is disclosed. The process, in one aspect, is able to receive a data stream formatted with soft information from a communication network such as a wireless network. After identifying a set of bits representing a first logic value from a portion of the data stream in accordance with a predefined soft encoding scheme, the set of bits is compressed into a compressed set of bits. The compressed set of bits which represents the first logic value is subsequently stored in a local memory.

Abstract: A new approach is proposed to support efficient convolution for deep learning by vectorizing multi-dimensional input data for multi-dimensional fast Fourier transform (FFT) and direct memory access (DMA) for data transfer. Specifically, a deep learning processor (DLP) includes a plurality of tensor engines each configured to perform convolution operations by applying one or more kernels on multi-dimensional input data for pattern recognition and classification based on a neural network, wherein each tensor engine includes, among other components, one or more vector processing engines each configured to vectorize the multi-dimensional input data at each layer of the neural network to generate a plurality of vectors and to perform multi-dimensional FFT on the generated vectors and/or the kernels to create output for the convolution operations. Each tensor engine further includes a data engine configured to prefetch the multi-dimensional data and/or the kernels to both on-chip and external memories via DMA.

Abstract: A process capable of employing compression and decompression mechanism to receive and decode soft information is disclosed. The process, in one aspect, is able to receive a data stream formatted with soft information from a communication network such as a wireless network. After identifying a set of bits representing a first logic value from a portion of the data stream in accordance with a predefined soft encoding scheme, the set of bits is compressed into a compressed set of bits. The compressed set of bits which represents the first logic value is subsequently stored in a local memory.

Abstract: Methods and apparatus for providing an FFT engine using a reconfigurable single delay feedback architecture. In one aspect, an apparatus includes a radix-2 (R2) single delay feedback (SDF) stage that generates a radix-2 output and a radix-3 (R3) SDF stage that generates a radix-3 output. The apparatus also includes one or more radix-2 squared (R2?2) SDF stages that generate a radix-4 output. The apparatus further includes a controller that configures a sequence of radix stages selected from the R2, R3, and R2?2 stages based on an FFT point size to form an FFT engine. The FFT engine receives input samples at a first stage of the sequence and generate an FFT output result that is output from a last stage of the sequence. The sequence includes no more than one R3 stage.

Abstract: Methods and apparatus for configuring a front end to process multiple sectors with multiple radio frequency frames. In an exemplary embodiment, a method includes decoding instructions included in a job description list, and configuring one or more processing functions of a transceiver to process a radio signal associated with a selected sector based on the decoded instructions. The configuration of the processing functions is synchronized according to time control instructions included in the job description list.

Abstract: A process capable of employing compression and decompression mechanism to receive and decode soft information is disclosed. Upon receiving a set of signals representing a logic value from a transmitter via a physical communication channel, the set of signals is demodulated in accordance with a soft decoding scheme and subsequently, a Log Likelihood Ratio (“LLR”) value representing the logic value is generated. After generating a quantized LLR value in response to the LLR value via a non-linear LLR quantizer, the quantized LLR value representing the compressed logic value is stored in a local storage.

Abstract: A process capable of employing compression and decompression mechanism to receive and decode soft information is disclosed. The process, in one aspect, is able to receive a data stream formatted with soft information from a communication network such as a wireless network. After identifying a set of bits representing a first logic value from a portion of the data stream in accordance with a predefined soft encoding scheme, the set of bits is compressed into a compressed set of bits. The compressed set of bits which represents the first logic value is subsequently stored in a local memory.

Abstract: A process capable of employing compression and decompression mechanism to receive and decode soft information is disclosed. The process, in one aspect, is able to receive a data stream formatted with soft information from a communication network such as a wireless network. After identifying a set of bits representing a first logic value from a portion of the data stream in accordance with a predefined soft encoding scheme, the set of bits is compressed into a compressed set of bits. The compressed set of bits which represents the first logic value is subsequently stored in a local memory.

Abstract: A process capable of employing compression and decompression mechanism to receive and decode soft information is disclosed. Upon receiving a set of signals representing a logic value from a transmitter via a physical communication channel, the set of signals is demodulated in accordance with a soft decoding scheme and subsequently, a Log Likelihood Ratio (“LLR”) value representing the logic value is generated. After generating a quantized LLR value in response to the LLR value via a non-linear LLR quantizer, the quantized LLR value representing the compressed logic value is stored in a local storage.