Advanced Micro Devices Corporation

Hewlett-packard Development Company, L.p.

Prakash Sathyanath Raghavendra

Arun C Ramachandran

Chandra Kumar Ramasamy

Keerthan Subraya Shagrithaya

Sandya Srivilliputtur Mannarswamy

Dibyendu Das

Sujoy Saraswati

Vasanthakumar Rajagopal

Virendra Kumar Mehta

Arun Rangasamy

Systems, methods, and devices for pruning a convolutional neural network (CNN). A subset of layers of the CNN is chosen, and for each layer of the subset of layers, how salient each filter in the layer is to an output of the CNN is determined, a subset of the filters in the layer is determined based on the salience of each filter in the layer, and the subset of filters in the layer is pruned. In some implementations, the layers of the subset of layers of the CNN are non-contiguous. In some implementations, the subset of layers includes odd numbered layers of the CNN and excludes even numbered layers of the CNN. In some implementations, the subset of layers includes even numbered layers of the CNN and excludes odd numbered layers of the CNN.

Accelerating neural networks with one shot skip layer pruning

Systems, apparatuses, and methods for implementing one-sided per-kernel clipping and weight transformation for neural networks are disclosed. Various parameters of a neural network are quantized from higher-bit representations to lower-bit representations to reduce memory utilization and power consumption. To exploit the effective range of quantized representations, positively biased weights are clipped and negated before convolution. Then, the results are rescaled back after convolution. A one-sided clipping technique is used for transforming weights to exploit the quantization range effectively, with the side chosen to be clipped being the biased side. This technique uses a global strategy for clipping without requiring skilled expertise. This approach allows the system to retain as much information as possible without losing unnecessary accuracy when quantizing parameters from higher-bit representations to lower-bit representations.

Efficient weight clipping for neural networks

A compiler generates transfer functions for blocks of a program during compilation of the program. The transfer functions estimate bit widths of variables in the blocks based on numbers of bits needed to carry out at least one instruction in the blocks and whether the variables are live in the blocks. For example, a transfer function may return a number indicating how many bits of a variable are needed to execute a current instruction as a function of the number of bits of the variable used by the program in subsequent instructions. Numbers of bits to represent the variables in the compiled program based on the transfer functions.

Estimation of bit widths of variables based on liveness

A system and method for improving run-time performance of applications with multithreaded and single threaded routines that are linked with libpthreads library is disclosed. In one embodiment, a method for running a mixed ST/MT application program linked with libpthreads library including creating an interceptor library containing pthread application programming interface (pthread_API) call interceptors and loading the interceptor library into the mixed ST/MT application program, and running the mixed ST/MT application program by using light weight (LW) and heavy weight (HW) synchronization routines based on determining switchovers between ST and MT program modes, respectively, during run-time using the interceptor library.

System and method for improving run-time performance of applications with multithreaded and single threaded routines

A technique for reducing non-local access, in dynamically generated code that resides in a code buffer of a non-uniform memory access computer system including multiple nodes, for improving overall performance of dynamic optimization systems. In one example embodiment, this is accomplished by partitioning the code buffer into multiple smaller code buffers and assigning each of the multiple smaller code buffers to one of the multiple nodes. Statically determining which methods in the generated code are executed by a thread and then to place those methods in associated one of the multiple smaller code buffers to reduce memory latencies introduced by non-local accesses.

Growing community of inventors

Bengaluru, India

Prakash Sathyanath Raghavendra