An enhanced dynamic random access memory (eDRAM)-based computing-in-memory (CIM) convolutional neural network (CNN) accelerator comprises four P2ARAM blocks, where each of the P2ARAM blocks includes a 5T1C ping-pong eDRAM bit cell array composed of 64×16 5T1C ping-pong eDRAM bit cells. In each of the P2ARAM blocks, 64×2 digital time converters convert a 4-bit activation value into different pulse widths from a row direction and input the pulse widths into the 5T1C ping-pong eDRAM bit cell array for calculation. A total of 16×2 convolution results are output in a column direction of the 5T1C ping-pong eDRAM bit cell array. The CNN accelerator uses the 5T1C ping-pong eDRAM bit cells to perform multi-bit storage and convolution in parallel. An S2M-ADC scheme is proposed to allot an area of an input sampling capacitor of an ABL to sign-numerical SAR ADC units of a C-DAC array without adding area overhead.

An enhanced dynamic random access memory (eDRAM)-based computing-in-memory (CIM) convolutional neural network (CNN) accelerator comprises four P2ARAM blocks, where each of the P2ARAM blocks includes a 5T1C ping-pong eDRAM bit cell array composed of 64×16 5T1C ping-pong eDRAM bit cells. In each of the P2ARAM blocks, 64×2 digital time converters convert a 4-bit activation value into different pulse widths from a row direction and input the pulse widths into the 5T1C ping-pong eDRAM bit cell array for calculation. A total of 16×2 convolution results are output in a column direction of the 5T1C ping-pong eDRAM bit cell array. The CNN accelerator uses the 5T1C ping-pong eDRAM bit cells to perform multi-bit storage and convolution in parallel. An S2M-ADC scheme is proposed to allot an area of an input sampling capacitor of an ABL to sign-numerical SAR ADC units of a C-DAC array without adding area overhead.

The present disclosure provides a method of producing a personalized medical record, comprising: sensing capabilities of a receiving device; retrieving stock information; retrieving personalized information; combining at least a portion of the stock information and at least a portion of the personalized information into the personalized record; formatting the personalized record based on a combination of the capabilities of the receiving device and a user's preference; and, transmitting the formatted personalized record to the device.

An integrated circuit comprising a plurality MAC pipelines wherein each MAC pipeline includes: (i) a plurality of MACs connected in series and (ii) a plurality of data paths including an accumulation data path, wherein each MAC includes a multiplier to multiply to generate product data and an accumulator to generate sum data. The integrated circuit further comprises a plurality of control/configure circuits, wherein each control/configure circuit connects directly to and is associated with a MAC pipeline, wherein each control/configure circuit includes an accumulation data path which is configurable to directly connect to the accumulation data path of the MAC pipeline to form an accumulation ring when the control/configure circuit is configured in an accumulation mode, and an output data path configurable to directly connect to the output of the accumulation data path of the MAC pipeline when the control/configure circuit is configured in an output data mode.

An integrated circuit comprising a plurality MAC pipelines wherein each MAC pipeline includes: (i) a plurality of MACs connected in series and (ii) a plurality of data paths including an accumulation data path, wherein each MAC includes a multiplier to multiply to generate product data and an accumulator to generate sum data. The integrated circuit further comprises a plurality of control/configure circuits, wherein each control/configure circuit connects directly to and is associated with a MAC pipeline, wherein each control/configure circuit includes an accumulation data path which is configurable to directly connect to the accumulation data path of the MAC pipeline to form an accumulation ring when the control/configure circuit is configured in an accumulation mode, and an output data path configurable to directly connect to the output of the accumulation data path of the MAC pipeline when the control/configure circuit is configured in an output data mode.

A First In First Out (FIFO) memory includes storage units. Outputs of the storage units are connected to one node. The storage unit includes storage sub-units, a selector, and a drive. An input of the selector is connected to outputs of the storage sub-units. An input of the drive is connected to an output of the selector. Driven by a first pointer signal, the storage sub-units receive storage data. Driven by a second pointer signal, the drive outputs the storage data.

The present embodiments relate to circuitry that efficiently performs floating-point arithmetic operations and fixed-point arithmetic operations. Such circuitry may be implemented in specialized processing blocks. If desired, the specialized processing blocks may include configurable interconnect circuitry to support a variety of different use modes. For example, the specialized processing block may efficiently perform a fixed-point or floating-point addition operation or a portion thereof, a fixed-point or floating-point multiplication operation or a portion thereof, a fixed-point or floating-point multiply-add operation or a portion thereof, just to name a few. In some embodiments, two or more specialized processing blocks may be arranged in a cascade chain and perform together more complex operations such as a recursive mode dot product of two vectors of floating-point numbers or a Radix-2 Butterfly circuit, just to name a few.

The present embodiments relate to circuitry that efficiently performs floating-point arithmetic operations and fixed-point arithmetic operations. Such circuitry may be implemented in specialized processing blocks. If desired, the specialized processing blocks may include configurable interconnect circuitry to support a variety of different use modes. For example, the specialized processing block may efficiently perform a fixed-point or floating-point addition operation or a portion thereof, a fixed-point or floating-point multiplication operation or a portion thereof, a fixed-point or floating-point multiply-add operation or a portion thereof, just to name a few. In some embodiments, two or more specialized processing blocks may be arranged in a cascade chain and perform together more complex operations such as a recursive mode dot product of two vectors of floating-point numbers or a Radix-2 Butterfly circuit, just to name a few.

The invention relates to a device for neurovascular stimulation, at least comprising: at least one brain activity sensor, at least one cardiovascular sensor, at least one computing unit and at least one output unit. The computing unit comprises at least one task algorithm, wherein signals of at least the brain activity sensor and the cardiovascular sensor can be received by the computing unit, and wherein a task, which is in correlation with at least the signals from at least the brain activity sensor and the signals of the cardiovascular sensor, can be determined by means of the task algorithm and can be output by means of the output unit.

The invention relates to a device for neurovascular stimulation, at least comprising: at least one brain activity sensor, at least one cardiovascular sensor, at least one computing unit and at least one output unit. The computing unit comprises at least one task algorithm, wherein signals of at least the brain activity sensor and the cardiovascular sensor can be received by the computing unit, and wherein a task, which is in correlation with at least the signals from at least the brain activity sensor and the signals of the cardiovascular sensor, can be determined by means of the task algorithm and can be output by means of the output unit.