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Hardware Architecture and Software Stack for PIM Based on Commercial DRAM Technology


Abstract: "Emerging applications such as deep neural network demand high off-chip memory bandwidth. However, under stringent physical constraints of chip packages and system boards, it becomes very expensive to further increase the bandwidth of off-chip memory. Besides, transferring data across the memory hierarchy constitutes a large fraction of total energy consumption of systems, and the ... » read more

NN-Baton: DNN Workload Orchestration & Chiplet Granularity Exploration for Multichip Accelerators


"Abstract—The revolution of machine learning poses an unprecedented demand for computation resources, urging more transistors on a single monolithic chip, which is not sustainable in the Post-Moore era. The multichip integration with small functional dies, called chiplets, can reduce the manufacturing cost, improve the fabrication yield, and achieve die-level reuse for different system scales... » read more

Xilinx AI Engines And Their Applications


This white paper explores the architecture, applications, and benefits of using Xilinx's new AI Engine for compute intensive applications like 5G cellular and machine learning DNN/CNN. 5G requires between five to 10 times higher compute density when compared with prior generations; AI Engines have been optimized for DSP, meeting both the throughput and compute requirements to deliver the hig... » read more

Domain-Specific Memory


Domain-specific computing may be all the rage, but it is avoiding the real problem. The bigger concern is the memories that throttle processor performance, consume more power, and take up the most chip area. Memories need to break free from the rigid structures preferred by existing software. When algorithms and memory are designed together, improvements in performance are significant and pr... » read more

Spiking Neural Networks Place Data In Time


Artificial neural networks have found a variety of commercial applications, from facial recognition to recommendation engines. Compute-in-memory accelerators seek to improve the computational efficiency of these networks by helping to overcome the von Neumann bottleneck. But the success of artificial neural networks also highlights their inadequacies. They replicate only a small subset of th... » read more

What Machine Learning Can Do In Fabs


Semiconductor Engineering sat down to discuss the issues and challenges with machine learning in semiconductor manufacturing with Kurt Ronse, director of the advanced lithography program at Imec; Yudong Hao, senior director of marketing at Onto Innovation; Romain Roux, data scientist at Mycronic; and Aki Fujimura, chief executive of D2S. What follows are excerpts of that conversation. L-R:... » read more

TOPS, Memory, Throughput And Inference Efficiency


Dozens of companies have or are developing IP and chips for Neural Network Inference. Almost every AI company gives TOPS but little other information. What is TOPS? It means Trillions or Tera Operations per Second. It is primarily a measure of the maximum achievable throughput but not a measure of actual throughput. Most operations are MACs (multiply/accumulates), so TOPS = (number of MAC... » read more

Aspinity’s Analog Neural Net Wake-Up Call


Putting an analog chip in front of an always-on system for digitizing speech and having the analog chip listen for sounds of interest may help avoid huge power waste and data congestion in current voice-recognition systems. Aspinity, an analog neuromorphic semiconductor startup, has worked the problem and just announced its Reconfigurable Analog Modular Processor (RAMP) platform yesterday. RAMP... » read more

AiMotive Is EDA For Self-Driving Cars


The team at aiMotive, a tool and IP company for OEMs making automated vehicles, isn’t waiting for smart infrastructure or 5G to make self-driving cars possible. The four-year-old startup based in Budapest, Hungary, is taking a self-sustainable route for the foreseeable future. The key to staying in business is not to compete with Waymo, Cruise or automotive companies, but to build the softwar... » read more

Machine Learning on Arm Cortex-M Microcontrollers


Machine learning (ML) algorithms are moving to the IoT edge due to various considerations such as latency, power consumption, cost, network bandwidth, reliability, privacy and security. Hence, there is an increasing interest in developing Neural Network (NN) solutions to deploy them on low-power edge devices such as the Arm Cortex-M microcontroller systems. CMSIS-NN is an open-source library of... » read more

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