James Clarkson

h-index5
2papers
167citations

2 Papers

9.3MES-HALLJun 19
Solid-state transcapacitor, a new gain element for logic, memory and interconnects

Amrita Mathuriya, Roza Kotlyar, Neal Reynolds et al.

Today's transistors dictate the voltage and charge scales for both logic and memory. While AI systems are recognized to be limited by memory energy, the dominant share of the energy is expended in the intrachip interconnects whose voltage and charge scales are set by transistors. The energy scaling challenges of transistors can be attributed to simultaneously meeting high current density, high current/impedance modulation, and the inability to lower voltages. Hence, a new logic element that lowers the voltage and charge needs is a priority, not only for lowering logic power but also memory access power. Here, we propose a novel 3-terminal logic element for low energy computing, a solid-state transcapacitor (TCAP). A TCAP is a solid state displacement current modulator realized by a gate which controls the charge-voltage relationship of the channel. Unlike transistors, TCAPs eliminate the dissipative transport current, are not bound by the Boltzmann current modulation limit, and operate with displacement currents limited only by the polarization response and contact resistance. Hence, TCAP circuits may simultaneously overcome the voltage, current density, and current modulation limits of CMOS. We describe a solid state TCAP using a piezoelectric transcapacitor in which a gate-controlled stressor modulates the capacitance of a polar channel via electromechanical coupling. This device achieves inversion and gain, essential for logic, and is functionally equivalent to a 1T-1C memory cell, enabling dense memory. Using voltage scaling, capacitive energy recovery, and high polarization densities of polar materials, the logic based on TCAP offers a pathway to 100 fold lower energy consumption with a delay comparable to ultimately scaled CMOS devices. This approach provides a new potential pathway for low-energy computing beyond the limits of transistors using electro-mechanics and multiferroics.

1.7CVAug 20, 2018
Navigating the Landscape for Real-time Localisation and Mapping for Robotics and Virtual and Augmented Reality

Sajad Saeedi, Bruno Bodin, Harry Wagstaff et al.

Visual understanding of 3D environments in real-time, at low power, is a huge computational challenge. Often referred to as SLAM (Simultaneous Localisation and Mapping), it is central to applications spanning domestic and industrial robotics, autonomous vehicles, virtual and augmented reality. This paper describes the results of a major research effort to assemble the algorithms, architectures, tools, and systems software needed to enable delivery of SLAM, by supporting applications specialists in selecting and configuring the appropriate algorithm and the appropriate hardware, and compilation pathway, to meet their performance, accuracy, and energy consumption goals. The major contributions we present are (1) tools and methodology for systematic quantitative evaluation of SLAM algorithms, (2) automated, machine-learning-guided exploration of the algorithmic and implementation design space with respect to multiple objectives, (3) end-to-end simulation tools to enable optimisation of heterogeneous, accelerated architectures for the specific algorithmic requirements of the various SLAM algorithmic approaches, and (4) tools for delivering, where appropriate, accelerated, adaptive SLAM solutions in a managed, JIT-compiled, adaptive runtime context.