A system, method, and computer program product are provided for a memory system. The system includes a first semiconductor platform including at least one first circuit, and at least one additional semiconductor platform stacked with the first semiconductor platform and including at least one additional circuit.

System, method, and computer program product for improving memory systems

The advent of multicore processors has renewed interest in the idea of incorporating transactions into the programming model used to write parallel programs. This approach, known as transactional memory, offers an alternative, and hopefully better, way to coordinate concurrent threads. The ACI (atomicity, consistency, isolation) properties of transactions provide a foundation to ensure that concurrent reads and writes of shared data do not produce inconsistent or incorrect results. At a higher level, a computation wrapped in a transaction executes atomically - either it completes successfully and commits its result in its entirety or it aborts. In addition, isolation ensures the transaction produces the same result as if no other transactions were executing concurrently. Although transactions are not a parallel programming panacea, they shift much of the burden of synchronizing and coordinating parallel computations from a programmer to a compiler, to a language runtime system, or to hardware. The challenge for the system implementers is to build an efficient transactional memory infrastructure. This book presents an overview of the state of the art in the design and implementation of transactional memory systems, as of early spring 2010. Table of Contents: Introduction / Basic Transactions / Building on Basic Transactions / Software Transactional Memory / Hardware-Supported Transactional Memory / Conclusions

Transactional Memory, 2nd Edition

Transactional memory

An electronic device and a control method thereof includes an infrared module which receives an infrared control signal having a predetermined section-specific waveform from an infrared transmitter, a switching unit which turns on/off the infrared module, and a controller which controls the switching unit to alternately turn on and off the infrared module in a predetermined cycle for recognizing the predetermined section-specific waveform of the infrared control signal. Thus, electric power consumed by an infrared module in a standby mode may be reduced while receiving a signal of an infrared transmitter.

Electronic device and control method thereof

Disclosed are a display apparatus and a method of controlling the display apparatus. The display apparatus includes: a signal receiver configured to receive a broadcasting signal; a display configured to display an image based on the received broadcasting signal; a sound receiver configured to receive a sound spoken by a user; a first sound recognizer configured to be supplied with power when the display apparatus is in a standby mode, and determine whether the received sound is a reserved word candidate having a high probability of corresponding to a reserved word; a second sound recognizer configured to be supplied with power when the received sound is determined as the reserved word candidate and to determine whether the received sound is the reserved word; and a controller configured to control the preset operation to be performed when the received sound is determined as the reserved word.

Electronic apparatus and control method thereof

A processor includes a processor core and a calculation circuit. The processor core includes logic determine a set of weights for use in a convolutional neural network (CNN) calculation and scale up the weights using a scale value. The calculation circuit includes logic to receive the scale value, the set of weights, and a set of input values, wherein each input value and associated weight of a same fixed size. The calculation circuit also includes logic to determine results from convolutional neural network (CNN) calculations based upon the set of weights applied to the set of input values, scale down the results using the scale value, truncate the scaled down results to the fixed size, and communicatively couple the truncated results to an output for a layer of the CNN.

Weight-shifting mechanism for convolutional neural networks

Version management, one of the key design dimensions of Hardware Transactional Memory (HTM) systems, defines where and how transactional modifications are stored. Current HTM systems use either eager or lazy version management. Eager systems that keep new values in-place while they hold old values in a software log, suffer long delays when aborts are frequent because the pre-transactional state is recovered by software. Lazy systems that buffer new values in specialized hardware offer complex and inefficient solutions to handle hardware overflows, which are common in applications with coarse-grain transactions. In this paper, we present FASTM, an eager log-based HTM that takes advantage of the processor’s cache hierarchy to provide fast abort recovery. FASTM uses a novel coherence protocol to buffer the transactional modifications in the first level cache and to keep the non-speculative values in the higher levels of the memory hierarchy. This mechanism allows fast abort recovery of transactions that do not overflow the first level cache resources. Contrary to lazy HTM systems, committing transactions do not have to perform any actions in order to make their results visible to the rest of the system. FASTM keeps the pre-transactional state in a software-managed log as well, which permits the eviction of speculative values and enables transparent execution even in the case of cache overflow. This approach simplifies eviction policies without degrading performance, because it only falls back to a software abort recovery for transactions whose modified state has overflowed the cache. Simulation results show that FASTM achieves a speed-up of 43% compared to LogTM-SE, improving the scalability of applications with coarse-grain transactions and obtaining similar performance to an ideal eager HTM with zero-cost abort recovery.

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FASTM: A Log-based Hardware Transactional Memory with Fast Abort Recovery

A storage device and method are described for performing convolution operations. For example, one embodiment of an apparatus to perform convolution operations comprises a plurality of processing units to execute convolution operations on input data and partial results; a unified scratchpad memory comprising a plurality of memory banks communicatively coupled to the plurality of processing units through a plurality of read/write ports, each of the plurality of memory banks partitioned to store both the input data and partial results; a control unit to allocate the input data and partial results to the memory banks to ensure a minimum quality of service in accordance with the specified number of read/write ports and the specified convolution operation to be performed.

Storage device and method for performing convolution operations

Most Hardware Transactional Memory (HTM) implementations choose fixed version and conflict management policies at design time. While eager HTM systems store transactional state in-place in memory and resolve conflicts when they are produced, lazy HTM systems buffer the transactional state in specialized hardware and defer the resolution of conflicts until commit time. Each scheme has its strengths and weaknesses, but, unfortunately, both approaches are too inflexible in the way they manage data versioning and transactional contention. Thus, fixed HTM systems may result in a significant performance opportunity loss when they execute complex transactional applications. In this paper, we present DynTM (Dynamically Adaptable HTM), the first fully-flexible HTM system that permits the simultaneous execution of transactions using complementary version and conflict management strategies. In the heart of DynTM is a novel coherence protocol that allows tracking conflicts among eager and lazy transactions. Both the eager and the lazy execution modes of DynTM exhibit very high performance compared to modern HTM systems. For example, the DynTM lazy execution mode implements local commits to improve on previous proposals. In addition, lazy transactions share the majority of hardware support with eager transactions, reducing substantially the hardware cost compared to other lazy HTM systems. By utilizing a simple predictor to decide the best execution mode for each transaction at runtime, DynTM obtains an average speedup of 34% over HTM systems that employ fixed version and conflict management policies.

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Marc Lupon

Papers

Weight-shifting mechanism for convolutional neural networks

FASTM: A Log-based Hardware Transactional Memory with Fast Abort Recovery

Storage device and method for performing convolution operations

A Dynamically Adaptable Hardware Transactional Memory

A Dynamically Adaptable Hardware Transactional Memory