Essays about: "memory optimization."

Showing result 1 - 5 of 132 essays containing the words memory optimization..

  1. 1. Optical Communication using Nanowires and Molecular Memory Systems

    University essay from Lunds universitet/Fysiska institutionen; Lunds universitet/Synkrotronljusfysik

    Author : Thomas Kjellberg Jensen; [2024]
    Keywords : neuromorphic computing; nanowire; molecular dye; DASA photoswitch; OBIC; Physics and Astronomy;

    Abstract : Neuromorphic computational networks, inspired by biological neural networks, provide a possible way of lowering computational energy cost, while at the same time allowing for much more sophisticated devices capable of real-time inferences and learning. Since simulating artificial neural networks on conventional computers is particularly inefficient, the development of neuromorphic devices is strongly motivated as the reliance on AI-models increases. READ MORE

  2. 2. Optimizing on-chip Machine Learning for Data Prefetching

    University essay from Göteborgs universitet/Institutionen för data- och informationsteknik

    Author : Hampus Larsson; Miranda Jernberg; Albin Pansell; Fabian Stigsson; Fredrik Hamrefors; Pontus Söderström; [2023-03-03]
    Keywords : Data Prefetching; Machine Learning; HW SW co-Design; HLS; FPGA;

    Abstract : The idea behind data prefetching is to speed up program execution by predicting what data is needed by the processor, before it is actually needed. Data prefetching is commonly performed by prefetching the next memory address in line, but there are other, more sophisticated approaches such as machine learning. READ MORE

  3. 3. EMONAS : Evolutionary Multi-objective Neuron Architecture Search of Deep Neural Network

    University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Author : Jiayi Feng; [2023]
    Keywords : DNN Deep Neural Network ; NAS Neural Architecture Search ; EA Evolutionary Algorithm ; Multi-Objective Optimization; Binary One Optimization; Embedded Systems; DNN Deep Neural Network ; NAS Neural Architecture Search ; EA Evolutionary Algorithm ; Multi-Objective Optimization; Binary One Optimization; Inbyggda system;

    Abstract : Customized Deep Neural Network (DNN) accelerators have been increasingly popular in various applications, from autonomous driving and natural language processing to healthcare and finance, etc. However, deploying them directly on embedded system peripherals within real-time operating systems (RTOS) is not easy due to the paradox of the complexity of DNNs and the simplicity of embedded system devices. READ MORE

  4. 4. A Conjugate Residual Solver with Kernel Fusion for massive MIMO Detection

    University essay from Högskolan i Halmstad/Centrum för forskning om tillämpade intelligenta system (CAISR)

    Author : Ioannis Broumas; [2023]
    Keywords : MIMO; massive MIMO; GPU; CUDA; Software Defined Radio; SDR; MMSE; ZF; zero-forcing; parallel detection; iterative methods; conjugate residual; parallel computing; kernel fusion;

    Abstract : This thesis presents a comparison of a GPU implementation of the Conjugate Residual method as a sequence of generic library kernels against implementations ofthe method with custom kernels to expose the performance gains of a keyoptimization strategy, kernel fusion, for memory-bound operations which is to makeefficient reuse of the processed data. For massive MIMO the iterative solver is to be employed at the linear detection stageto overcome the computational bottleneck of the matrix inversion required in theequalization process, which is 𝒪(𝑛3) for direct solvers. READ MORE

  5. 5. RocksDB Read Optimization Strategies for Streaming Applications

    University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Author : Björkman Fredrik; [2023]
    Keywords : RocksDB; Data streams; Micro-batching; Data stream processing; read operation benchmark; Data stream workload simulation; RocksDB; Dataströmmar; Mikro-batching; Dataströmsprocessering; Läsoperationsmätresultat; Dataströmsarbetsbelastningssimulation;

    Abstract : Modern stream processors rely on embedded key-value stores to manage state that accumulates over long-running computations and exceeds the available memory size. One of these key-value stores is RocksDB, which is widely used in many applications requiring high-performing storage with low latency. READ MORE