tag: neural network

Domain-Specific Memory
By Brian Bailey - 11 Mar, 2021 - Comments: 2

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

Machine Learning Approach for Fast Electromigration Aware Aging Prediction in Incremental Design of Large Scale On-Chip Power Grid Network
By Technical Paper Link - 31 Dec, 2020 - Comments: 0

Abstract "With the advancement of technology nodes, Electromigration (EM) signoff has become increasingly difficult, which requires a considerable amount of time for an incremental change in the power grid (PG) network design in a chip. The traditional Black’s empirical equation and Blech’s criterion are still used for EM assessment, which is a time-consuming process. In this article, for ... » read more

Spiking Neural Networks Place Data In Time
By Katherine Derbyshire - 16 Jul, 2020 - Comments: 0

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
By Mark LaPedus - 25 Mar, 2020 - Comments: 0

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
By Geoff Tate - 02 Jul, 2019 - Comments: 3

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
By Susan Rambo - 26 Jun, 2019 - Comments: 0

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
By Susan Rambo - 17 Jun, 2019 - Comments: 0

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
By Arm - 10 Apr, 2019 - Comments: 0

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

Enabling Embedded Vision Neural Network DSPs
By Cadence - 19 Dec, 2018 - Comments: 0

Neural networks are now being developed in a variety of technology segments in the embedded market, from mobile to surveillance to the automotive segment. The computational and power requirements to process this data is increasing, with new methods to approach deep learning challenges emerging every day. Vision processing systems must be designed holistically, for all platforms, with hardwa... » read more

AI Architectures Must Change
By Brian Bailey - 13 Aug, 2018 - Comments: 4

Using existing architectures for solving machine learning and artificial intelligence problems is becoming impractical. The total energy consumed by AI is rising significantly, and CPUs and GPUs increasingly are looking like the wrong tools for the job. Several roundtables have concluded the best opportunity for significant change happens when there is no legacy IP. Most designs have evolved... » read more

← Older posts Newer posts →

  • Sponsors

About

Navigation

Connect With Us

Copyright ©2013-2024 SMG   |  Terms of Service  |  Privacy Policy