Printed Microprocessors

Bleier, Nathaniel; Mubarik, Muhammad Husnain; Rasheed, Farhan; Aghassi‐Hagmann, Jasmin; Tahoori, Mehdi B.; Kumar, Rakesh

doi:10.1109/isca45697.2020.00028

Cited by 20 publications

(22 citation statements)

References 82 publications

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“…The resulting RTL description of the pareto-optimal bespoke Decision Trees is automatically created, by parsing the tree structure, and synthesized using Synopsys Design Compiler, at a relaxed clock (i.e., 50ms across all circuits). All designs were mapped to the inkjet-printed Electrolyte Gated Transistor (EGT) library [2], described in Section II-A. Area measurements are directly obtained from the Design Compiler, where as power estimations are extracted using Synopsys Primetime.…”

Section: Experimental Evaluationmentioning

confidence: 99%

“…Printed electronics (PE) based on maskless and additive processes have emerged to overcome this obstacle, which offer significant cost reductions [1]. Printed electronics additionally offer stretchability, flexibility and porosity benefits, which are infeasible for silicon devices [2]. Note however, that PE elements are manufactured at the micrometer (um) scale and therefore cannot compete with their siliconbased counterparts (manufactured at nanometer scale) in terms of performance or power and area efficiency.…”

Section: Introductionmentioning

confidence: 99%

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Approximate Decision Trees For Machine Learning Classification on Tiny Printed Circuits

Balaskas¹,

Zervakis²,

Siozios³

et al. 2022

Preprint

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Although Printed Electronics (PE) cannot compete with silicon-based systems in conventional evaluation metrics, e.g., integration density, area and performance, PE offers attractive properties such as on-demand ultra-low-cost fabrication, flexibility and non-toxicity. As a result, it targets application domains that are untouchable by lithography-based silicon electronics and thus have not yet seen much proliferation of computing. However, despite the attractive characteristics of PE, the large feature sizes in PE prohibit the realization of complex printed circuits, such as Machine Learning (ML) classifiers. In this work, we exploit the hardware-friendly nature of Decision Trees for machine learning classification and leverage the hardware-efficiency of the approximate design in order to generate approximate ML classifiers that are suitable for tiny, ultra-resource constrained, and battery-powered printed applications.

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Section: Experimental Evaluationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Approximate Decision Trees For Machine Learning Classification on Tiny Printed Circuits

Balaskas¹,

Zervakis²,

Siozios³

et al. 2022

Preprint

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show abstract

“…Such domains have requirements for ultra-low cost and conformality that silicon-based systems cannot satisfy. For example, increased manufacturing, packaging, and assembly costs of silicon prevent sub-cent costs while silicon systems cannot meet either stretchability, porosity, and flexibility requirements [2].…”

Section: Introductionmentioning

confidence: 99%

Cross-Layer Approximation For Printed Machine Learning Circuits

Giorgos¹,

Zervakis²,

Soudris³

et al. 2022

Preprint

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Printed electronics (PE) feature low non-recurring engineering costs and low per unit-area fabrication costs, enabling thus extremely low-cost and on-demand hardware. Such low-cost fabrication allows for high customization that would be infeasible in silicon, and bespoke architectures prevail to improve the efficiency of emerging PE machine learning (ML) applications. However, even with bespoke architectures, the large feature sizes in PE constraint the complexity of the ML models that can be implemented. In this work, we bring together, for the first time, approximate computing and PE design targeting to enable complex ML models, such as Multi-Layer Perceptrons (MLPs) and Support Vector Machines (SVMs), in PE. To this end, we propose and implement a cross-layer approximation, tailored for bespoke ML architectures. At the algorithmic level we apply a hardware-driven coefficient approximation of the ML model and at the circuit level we apply a netlist pruning through a full search exploration. In our extensive experimental evaluation we consider 14 MLPs and SVMs and evaluate more than 4300 approximate and exact designs. Our results demonstrate that our cross approximation delivers Pareto optimal designs that, compared to the state-of-the-art exact designs, feature 47% and 44% average area and power reduction, respectively, and less than 1% accuracy loss.

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“…Z80 was very popular as a microprocessor not only in PC applications, but also in industrial embedded applications, and some of the big manufacturers have Z80 core inside their ASIC chips still today or use enhanced versions of Z80 in consumer electronic devices. [4][5][6][7] Zilog still manufactures ez80, an enhanced version of the original Z80, which is still being used by Texas Instruments in its TI-84 and TI-84 Plus calculators. 7 It is among the few silicon chips that made a remarkable impact on the electronic device industry.…”

mentioning

confidence: 99%

“…[4][5][6][7] Zilog still manufactures ez80, an enhanced version of the original Z80, which is still being used by Texas Instruments in its TI-84 and TI-84 Plus calculators. 7 It is among the few silicon chips that made a remarkable impact on the electronic device industry. 8 To this day, Zilog produces a range of Z80based microprocessors and intelligent peripheral controllers, and they are available from reputed electronics component suppliers.…”

mentioning

confidence: 99%

Z80—The 1970s Microprocessor Still Alive

Preethichandra

2021

IEEE Micro

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Printed Microprocessors

Cited by 20 publications

References 82 publications

Approximate Decision Trees For Machine Learning Classification on Tiny Printed Circuits

Approximate Decision Trees For Machine Learning Classification on Tiny Printed Circuits

Cross-Layer Approximation For Printed Machine Learning Circuits

Z80—The 1970s Microprocessor Still Alive

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