Xilinx, Inc.

Rajeev Patwari

Elliott Delaye

Xiao Teng

Ehsan Ghasemi

Jorn Tuyls

Sanket Pandit

Ephrem C Wu

Aaron Ng

Ashish Sirasao

Chaithanya Dudha

Krishna Garlapati

Mrinal J Sarmah

Nithin Kumar Guggilla

Satyaprakash Pareek

Shreyas Manjunath

Tejus Siddagangaiah

Vamsi Krishna Nalluri

Sai Lalith Chaitanya Ambatipudi

Sandeep Jayant Sathe

Bryan Lozano

Pramod Peethambaran

Hardware acceleration of machine learning (ML) designs includes translating an ML primitive into an intermediate representation. The intermediate representation is subdivided to specify a functional compute block. The functional compute block is sized according to a compute node primitive adapted for implementing the ML primitive on target hardware. An overlay is generated for the ML primitive, at least in part, by mapping the functional compute block to the compute node primitive. The overlay is synthesizable to implement the ML primitive on the target hardware. The overlay can be scheduled for operation within the target hardware as part of an ML design including the ML primitive.

Hardware acceleration of machine learning designs

Controlling a data processing (DP) array includes creating a replica of a register address space of the DP array based on the design and the DP array. A sequence of instructions, including write instructions and read instructions, is received. The write instructions correspond to buffer descriptors specifying runtime data movements for a design for a DP array. The write instructions are converted into transaction instructions and the read instructions are converted into wait instructions based on the replica of the register address space. The transaction instructions and the wait instructions are included in an instruction buffer. The instruction buffer is provided to a microcontroller configured to execute the transaction instructions and the wait instructions to implement the runtime data movements for the design as implemented in the DP array. In another aspect, the instruction buffer is stored in a file for subsequent execution by the microcontroller.

Instruction set architecture for data processing array control

Examples herein describe techniques for performing parallel processing using a plurality of processing elements (PEs) and a controller for data that has data dependencies. For example, a calculation may require an entire row or column to be summed, or to determine its mean. The PEs can be assigned different chunks of a data set (e.g., a tensor set, a column, or a row) for processing. The PEs can use one or more tokens to inform the controller when they are done with partial processing of their data chunks. The controller can then gather the partial results and determine an intermediate value for the data set. The controller can then distribute this intermediate value to the PEs which then re-process their respective data chunks using the intermediate value to generate final results.

Scalable acceleration of reentrant compute operations

An integrated circuit includes a plurality of kernels and a virtual machine coupled to the plurality of kernels. The virtual machine is configured to interpret instructions directed to different ones of the plurality of kernels. The virtual machine is configured to control operation of the different ones of the plurality of kernels responsive to the instructions.

Reconfigurable neural engine with extensible instruction set architecture

Approaches for data compression involve a compression circuit packing non-zero data elements of a succession of words of a plurality of blocks into packed words by packing non-zero data elements of one or more words of the succession in each packed word, and restricting each packed word to data elements of one uncompressed block. The compression circuit writes each packed word in a RAM and within a compressed address range associated with the uncompressed block when the packed word is full of non-zero data elements, or before the packed word is full if the next input word is of another uncompressed block.

Compression of sparse tensors

Determining on-chip memory access patterns can include modifying a circuit design to include a profiler circuit for a random-access memory (RAM) of the circuit design, wherein the profiler circuit is configured to monitor an address bus of the RAM, and modifying the circuit design to include a debug circuit connected to the profiler circuit. Usage data for the RAM can be generated by detecting, using the profiler circuit, addresses of the RAM accessed during a test of the circuit design, as implemented in an integrated circuit. The usage data for the RAM can be output using the debug circuit.

On-chip memory access pattern detection for power and resource reduction

Growing community of inventors

Campbell, CA, United States of America

Rajeev Patwari