5,558 research outputs found
POMDPs under Probabilistic Semantics
We consider partially observable Markov decision processes (POMDPs) with
limit-average payoff, where a reward value in the interval [0,1] is associated
to every transition, and the payoff of an infinite path is the long-run average
of the rewards. We consider two types of path constraints: (i) quantitative
constraint defines the set of paths where the payoff is at least a given
threshold lambda_1 in (0,1]; and (ii) qualitative constraint which is a special
case of quantitative constraint with lambda_1=1. We consider the computation of
the almost-sure winning set, where the controller needs to ensure that the path
constraint is satisfied with probability 1. Our main results for qualitative
path constraint are as follows: (i) the problem of deciding the existence of a
finite-memory controller is EXPTIME-complete; and (ii) the problem of deciding
the existence of an infinite-memory controller is undecidable. For quantitative
path constraint we show that the problem of deciding the existence of a
finite-memory controller is undecidable.Comment: Appears in Proceedings of the Twenty-Ninth Conference on Uncertainty
in Artificial Intelligence (UAI2013
Implementation of AMBA-ASB Memory Controller with Power Analysis
The work presented here is a summary of result obtained when AMBA-ASB memory controller was simulated and synthesized, using ModelSim 6.4a and Xilinx ISE 14.7. Memory controller is designed using master and slave circuit. Memory controller controls the flow of data from master to slave peripheral. Memory controller is a digital circuit. AMBA-ASB has several features i.e it provides parallel communication, high clock frequency, high performance system.
DOI: 10.17762/ijritcc2321-8169.150311
ISIM: The simulator for the impulse adaptable memory system
technical reportThis document describes ISIM, the simulator for the Impulse Adaptable Memory System. Impulse adds two new features to a conventional memory system. First, it supports a configurable, extra level of address remapping at the memory controller. Second, it supports prefetching at the memory controller. consequently, two new units, a remapping controller and a memory controller cache, are added to a traditional memory system to support the new Impulse features. ISIM is based on Paint, a PA-RISC instruction set interpreter. ISIM extends Paint with a detailed Impulse memory system model which includes a primary data cache, a secondary data cache, a system bus, an Impulse memory controller, and a renovated DRAM backend. Note that this document focuses on the Impulse extensions only. The reader should consult the Paint technical report [2] for an overview of the Paint simulation environment and terminology
Memory controller for vector processor
To manage power and memory wall affects, the HPC industry supports FPGA reconfigurable accelerators and vector processing cores for data-intensive scientific applications. FPGA based vector accelerators are used to increase the performance of high-performance application kernels. Adding more vector lanes does not affect the performance, if the processor/memory performance gap dominates. In addition if on/off-chip communication time becomes more critical than computation time, causes performance degradation. The system generates multiple delays due to application’s irregular data arrangement and complex scheduling scheme. Therefore, just like generic scalar processors, all sets of vector machine – vector supercomputers to vector microprocessors – are required to have data management and access units that improve the on/off-chip bandwidth and hide main memory latency. In this work, we propose an Advanced Programmable Vector Memory Controller (PVMC), which boosts noncontiguous vector data accesses by integrating descriptors of memory patterns, a specialized on-chip memory, a memory manager in hardware, and multiple DRAM controllers. We implemented and validated the proposed system on an Altera DE4 FPGA board. The PVMC is also integrated with ARM Cortex-A9 processor on Xilinx Zynq All-Programmable System on Chip architecture. We compare the performance of a system with vector and scalar processors without PVMC. When compared with a baseline vector system, the results show that the PVMC system transfers data sets up to 1.40x to 2.12x faster, achieves between 2.01x to 4.53x of speedup for 10 applications and consumes 2.56 to 4.04 times less energy.Peer ReviewedPostprint (author's final draft
Ramulator 2.0: A Modern, Modular, and Extensible DRAM Simulator
We present Ramulator 2.0, a highly modular and extensible DRAM simulator that
enables rapid and agile implementation and evaluation of design changes in the
memory controller and DRAM to meet the increasing research effort in improving
the performance, security, and reliability of memory systems. Ramulator 2.0
abstracts and models key components in a DRAM-based memory system and their
interactions into shared interfaces and independent implementations. Doing so
enables easy modification and extension of the modeled functions of the memory
controller and DRAM in Ramulator 2.0. The DRAM specification syntax of
Ramulator 2.0 is concise and human-readable, facilitating easy modifications
and extensions. Ramulator 2.0 implements a library of reusable templated lambda
functions to model the functionalities of DRAM commands to simplify the
implementation of new DRAM standards, including DDR5, LPDDR5, HBM3, and GDDR6.
We showcase Ramulator 2.0's modularity and extensibility by implementing and
evaluating a wide variety of RowHammer mitigation techniques that require
different memory controller design changes. These techniques are added
modularly as separate implementations without changing any code in the baseline
memory controller implementation. Ramulator 2.0 is rigorously validated and
maintains a fast simulation speed compared to existing cycle-accurate DRAM
simulators. Ramulator 2.0 is open-sourced under the permissive MIT license at
https://github.com/CMU-SAFARI/ramulator
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