Channel: PDP-11π
Apple M1, In-Depth Review
π½ CPU : 8 ARM cores = 4 high perf + 4 low power , 5nm, TSMC
π₯GPU Comparable with GTX 1650
πDRAM : 3DStack HBM, lower latency and power consumption
π Read more in Notion
π½ CPU : 8 ARM cores = 4 high perf + 4 low power , 5nm, TSMC
π₯GPU Comparable with GTX 1650
πDRAM : 3DStack HBM, lower latency and power consumption
π Read more in Notion
π’Quantum Annealing Simulation and FPGAs
While pure-play quantum computing (QC) gets most of the QC-related attention, thereβs also been steady progress adapting quantum methods for select use on classical computers.
World interest in Quantum Computing warms up the interest in Quantum-Inspired algorithms, among them Quantum Annealing Simulation(QA).
QA has nothing in common with qubits and Ρryocooler but offers a fast optimization method for complex but structured non-convex landscape.
Before moving further, we recommend you to read first about the Simulated Annealing because QA is a kind of extension of classical SA. Read here and here.
Analytical and numerical evidence suggests that quantum annealing outperforms simulated annealing under certain conditions See this short and clear Introduction to Quantum inspired Optimization
QA can be simulated on a computer using quantum Monte Carlo (QMC), but computational complexity scales up too fast. That's where application specific hardware comes out on scene
π¦FPGA
OpenCLβbased design of an FPGA accelerator for quantum annealing simulation
FPGA accelerator for QA simulations designed using Intel OpenCL HLS and achieved 6 times the multicore CPU implementation.
π¦¨Why not GPU?
None of these accelerators are suitable for complete graphs where every node has an interaction with all the other nodes. It is very difficult to accelerate QMC algorithm for complete graphs using GPUs due to the lack of SIMD operations and high data dependency
πFurther Reading:
πD-Wave Two -commercially available computer for QA simulation
πQuantum-inspired algorithms in practice
βοΈMicrosoft announced that Toshiba Bifurcation Machine
will be available through the Azure Quantum platform.
While pure-play quantum computing (QC) gets most of the QC-related attention, thereβs also been steady progress adapting quantum methods for select use on classical computers.
World interest in Quantum Computing warms up the interest in Quantum-Inspired algorithms, among them Quantum Annealing Simulation(QA).
QA has nothing in common with qubits and Ρryocooler but offers a fast optimization method for complex but structured non-convex landscape.
Before moving further, we recommend you to read first about the Simulated Annealing because QA is a kind of extension of classical SA. Read here and here.
Analytical and numerical evidence suggests that quantum annealing outperforms simulated annealing under certain conditions See this short and clear Introduction to Quantum inspired Optimization
QA can be simulated on a computer using quantum Monte Carlo (QMC), but computational complexity scales up too fast. That's where application specific hardware comes out on scene
π¦FPGA
OpenCLβbased design of an FPGA accelerator for quantum annealing simulation
FPGA accelerator for QA simulations designed using Intel OpenCL HLS and achieved 6 times the multicore CPU implementation.
π¦¨Why not GPU?
None of these accelerators are suitable for complete graphs where every node has an interaction with all the other nodes. It is very difficult to accelerate QMC algorithm for complete graphs using GPUs due to the lack of SIMD operations and high data dependency
πFurther Reading:
πD-Wave Two -commercially available computer for QA simulation
πQuantum-inspired algorithms in practice
βοΈMicrosoft announced that Toshiba Bifurcation Machine
will be available through the Azure Quantum platform.
Wikipedia
Quantum annealing
method for finding solutions to combinatorial optimisation problems and ground states of glassy systems using quantum fluctuations
PDP-11π
https://www.economist.com/technology-quarterly/2020/06/11/the-cost-of-training-machines-is-becoming-a-problem The growing demand for computing power has fuelled a boom in chip design and specialised devices that can perform the calculations used in AI efficiently.β¦
Graphcore raises $222M at $2.7B valuation
https://techcrunch-com.cdn.ampproject.org/c/s/techcrunch.com/2020/12/28/ai-chipmaker-graphcore-raises-222m-at-a-2-77b-valuation-and-puts-an-ipo-in-its-sights/amp/
https://techcrunch-com.cdn.ampproject.org/c/s/techcrunch.com/2020/12/28/ai-chipmaker-graphcore-raises-222m-at-a-2-77b-valuation-and-puts-an-ipo-in-its-sights/amp/
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