A compute-in-memory neural network architecture combines neural circuits implemented in CMOS technology and synaptic conductance crossbar arrays. The crossbar memory structures store the weight parameters of the neural network in the conductances of the synapse elements, which define interconnects between...
Unprecedented choice in architecture to solve for any compute need. Leadership Across the Compute Spectrum The range of computing applications today is incredibly varied—and it’s growing more so, especially with the proliferation of data, edge computing, and artificial intelligence. However, different...
CLDNN__ARCHITECTURE_TARGETSTRINGArchitecture of target system (where binary output will be deployed). CMake will try to detect it automatically (based on selected generator type, host OS and compiler properties). Specify this option only if CMake has problem with detection. Currently supported:Windo...
Designing a Performance-Centric MAC Unit with Pipelined Architecture for DNN Accelerators In order to improve the performance of deep neural network (DNN) accelerators, it is necessary to optimize compute efficiency and operating frequency. Howe... G Raut,J Mukala,V Sharma,... - 《Circuits System...
Analogue in-memory computing (AIMC) with resistive memory devices could reduce the latency and energy consumption of deep neural network inference tasks by directly performing computations within memory. However, to achieve end-to-end improvements in latency and energy consumption, AIMC must be combined...
Support for multiple data types: FP32, FP16, INT8, UINT8, BFLOAT16 Micro-architecture optimization for key ML primitives Highly configurable build options enabling lightweight binaries Advanced optimization techniques such as kernel fusion, Fast math enablement and texture utilization ...
This has found wide-spread adoption in accelerating neural networks for machine learning applications. Utilizing a crossbar architecture with emerging non-volatile memories (eNVM) such as dense resistive random access memory (RRAM) or phase... B Crafton,S Spetalnick,A Raychowdhury 被引量: 0发表...
Heterogeneous Computing Solving complex mathematical tasks by breaking larger functions into smaller ones and assigning them to available processors of different types. "We're rethinking computing itself from the bottom up by applying the latest understanding from neuroscience to computer architecture." ...
These improve data-level parallelism, which is an indicator of data-intensive computing, an execution performance just like a deep neural network task. In addition, Compute Unified Device Architecture (CUDA) is introduced. CUDA is NVIDIA's common programming infrastructure for data-intensive computing...
Founded in February 2021, PIMCHIP positions itself as an innovator in intelligent computing architecture, dedicated to providing technological support for the widespread application of AI through innovative memory-compute solutions. The company has applied for over 40 patents domestically and internationally...