Advancing Hardware Security Using Polymorphic and Stochastic Spin-Hall Effect Devices

Protecting intellectual property (IP) in electronic circuits has become a serious challenge in recent years. Logic locking/encryption and layout camouflaging are two prominent techniques for IP protection. Most existing approaches, however, particularly those focused on CMOS integration, incur excessive design overheads resulting from their need for additional circuit structures or device-level modifications. This work leverages the innate polymorphism of an emerging spin-based device, called the giant spin-Hall effect (GSHE) switch, to simultaneously enable locking and camouflaging within a single instance. Using the GSHE switch, we propose a powerful primitive that enables cloaking all the 16 Boolean functions possible for two inputs. We conduct a comprehensive study using state-of-the-art Boolean satisfiability (SAT) attacks to demonstrate the superior resilience of the proposed primitive in comparison to several others in the literature. While we tailor the primitive for deterministic computation, it can readily support stochastic computation; we argue that stochastic behavior can break most, if not all, existing SAT attacks. Finally, we discuss the resilience of the primitive against various side-channel attacks as well as invasive monitoring at runtime, which are arguably even more concerning threats than SAT attacks.Comment: Published in Proc. Design, Automation and Test in Europe (DATE) 201

New Data Structures and Algorithms for Logic Synthesis and Verification

The strong interaction between Electronic Design Automation (EDA) tools and Complementary Metal-Oxide Semiconductor (CMOS) technology contributed substantially to the advancement of modern digital electronics. The continuous downscaling of CMOS Field Effect Transistor (FET) dimensions enabled the semiconductor industry to fabricate digital systems with higher circuit density at reduced costs. To keep pace with technology, EDA tools are challenged to handle both digital designs with growing functionality and device models of increasing complexity. Nevertheless, whereas the downscaling of CMOS technology is requiring more complex physical design models, the logic abstraction of a transistor as a switch has not changed even with the introduction of 3D FinFET technology. As a consequence, modern EDA tools are fine tuned for CMOS technology and the underlying design methodologies are based on CMOS logic primitives, i.e., negative unate logic functions. While it is clear that CMOS logic primitives will be the ultimate building blocks for digital systems in the next ten years, no evidence is provided that CMOS logic primitives are also the optimal basis for EDA software. In EDA, the efficiency of methods and tools is measured by different metrics such as (i) the result quality, for example the performance of a digital circuit, (ii) the runtime and (iii) the memory footprint on the host computer. With the aim to optimize these metrics, the accordance to a specific logic model is no longer important. Indeed, the key to the success of an EDA technique is the expressive power of the logic primitives handling and solving the problem, which determines the capability to reach better metrics. In this thesis, we investigate new logic primitives for electronic design automation tools. We improve the efficiency of logic representation, manipulation and optimization tasks by taking advantage of majority and biconditional logic primitives. We develop synthesis tools exploiting the majority and biconditional expressiveness. Our tools show strong results as compared to state-of-the-art academic and commercial synthesis tools. Indeed, we produce the best results for several public benchmarks. On top of the enhanced synthesis power, our methods are the natural and native logic abstraction for circuit design in emerging nanotechnologies, where majority and biconditional logic are the primitive gates for physical implementation. We accelerate formal methods by (i) studying properties of logic circuits and (ii) developing new frameworks for logic reasoning engines. We prove non-trivial dualities for the property checking problem in logic circuits. Our findings enable sensible speed-ups in solving circuit satisfiability. We develop an alternative Boolean satisfiability framework based on majority functions. We prove that the general problem is still intractable but we show practical restrictions that can be solved efficiently. Finally, we focus on reversible logic where we propose a new equivalence checking approach. We exploit the invertibility of computation and the functionality of reversible gates in the formulation of the problem. This enables one order of magnitude speed up, as compared to the state-of-the-art solution. We argue that new approaches to solve EDA problems are necessary, as we have reached a point of technology where keeping pace with design goals is tougher than ever

A Circuit Synthesis Flow for Controllable-Polarity Transistors

Double-Gate (DG) controllable-polarity Field-Effect Transistors (FETs) are devices whose n- or p- polarity is on-line configurable by adjusting the second gate voltage. Such emerging transistors have been fabricated in silicon nanowires, carbon nanotuges and graphene technologies. Thanks to their enhanced functionality, DG controllable-polarity FETs implement arith- metic functions, such as XOR and MAJ, with limited physical resources enabling compact and high-performance datapaths. In order to design digital circuits with this technology, automated design techniques are of paramount importance. In this paper, we describe a design automation framework for DG controllable- polarity transistors. First, we present a novel dedicated logic representation form capable to exploit the polarity control during logic synthesis. Then, we tackle challenges at the physical level, presenting a regular layout technique that alleviates the interconnection issue deriving from the second gate routing. We use logic and physical synthesis tools to form a complete design automation flow. Experimental results show that the proposed flow is able to reduce the area and delay of digital circuits, based on 22-nm DG controllable-polarity SiNWFETs, by 22% and 42%, respectively, as compared to a commercial synthesis tool. With respect to a 22-nm FinFET technology, the proposed flow produces circuits, based on 22-nm DG controllable-polarity SiNWFETs, with 2.9x smaller area-delay product

A New Basic Logic Structure for Data-Path Computation

Nowadays, Field Programmable Gate Arrays (FPGA) implement arithmetic functions using specific circuits at the logic block level, such as the carry paths, or at the structure level adopting Digital Signal Processing (DSP) blocks. Nevertheless, all these approaches, introduced to ease the realization of specific functions, are lacking of generality. In this paper, we introduce a new logic block that natively realizes arithmetic functions while preserving the versatility to implement general logic functions. It consists of a partially interconnected matrix of signal routers driven by comparators. We demonstrate that this structure can realize (i) any 2-output 2-input logic function or (ii) any single-output 3-input logic function or (iii) specific logic, such as arithmetic functions, with up to 4-output and 8-inputs. As compared to a standard 6-input Look Up Table (LUT), the proposed block requires roughly the same area but is 35.3% faster. Even though the proposed block has not the same exhaustive configurability of a 6-input LUT, there are arithmetic functions realizable in a single block that do not fit in one, or even more, 6-input LUT. For example, a single block inherently implements an entire 3-bit adder that requires 3× more resources with LUTs plus also custom circuitry. From a system level perspective, we show that a 256-bit adder is implemented with a gain on area×delay product of 31% as compared to its traditional LUT-based counterpart

Majority Logic Representation and Satisfiability

Majority logic is a powerful generalization of common AND/OR logic. Original two-level and multi-level logic networks can use majority operators as primitive connective, in place of AND/ORs. In such a way, Boolean functions have novel means for compact representation and efficient manipulation. In this paper, we focus on two-level logic representation. We define a Majority Normal Form (MNF), as an alternative to traditional Disjunctive Normal Form (DNF) and Conjunctive Normal Form (CNF). After a brief investigation on the MNF expressive power, we study the problem of MNF-SATisfiability (MNF-SAT). We prove that MNF-SAT is NP-complete, as its CNF-SAT counterpart. However, we show practical restrictions on MNF formula whose satisfiability can be decided in polynomial time. We finally propose a simple algorithm to solve MNF- SAT, based on the intrinsic functionality of two-level majority logic. Although an automated MNF-SAT solver is still under construction, manual examples already demonstrate promising opportunities

Majority-Inverter Graph: A Novel Data-Structure and Algorithms for Efficient Logic Optimization

In this paper, we present Majority-Inverter Graph (MIG), a novel logic representation structure for efficient optimization of Boolean functions. An MIG is a directed acyclic graph consisting of three-input majority nodes and regular/complemented edges. We show that MIGs include any AND/OR/Inverter Graphs (AOIGs), containing also the well- known AIGs. In order to support the natural manipulation of MIGs, we introduce a new Boolean algebra, based exclusively on majority and inverter operations, with a complete axiomatic system. Theoretical results show that it is possible to explore the entire MIG representation space by using only five primitive transformation rules. Such feature opens up a great opportunity for logic optimization and synthesis. We showcase the MIG potential by proposing a delay-oriented optimization technique. Experimental results over MCNC benchmarks show that MIG optimization reduces the number of logic levels by 18%, on average, with respect to AIG optimization performed by ABC academic tool. Employed in a traditional optimization-mapping circuit synthesis flow, MIG optimization enables an average reduction of {22%, 14%, 11%} in the estimated {delay, area, power} metrics, before physical design, as compared to academic/commercial synthesis flows

Unlocking Controllable-Polarity Transistors Opportunities by Exclusive-OR and Majority Logic Synthesis

For more than four decades, Complementary Metal-Oxide- Semiconductor (CMOS) Field Effect Transistors (FETs) have been the baseline technology for implementing digital computation systems. CMOS transistors natively implement Not-AND (NAND)- and Not- OR (NOR)-based logic operators. Nowadays, we observe a trend towards devices with an increased set of logic capabilities, i.e., with the ability to realize in a compact way specific logic operators as compared to the standard CMOS. In particular, controllable-polarity devices enable a native and compact realization of eXclusive-OR (XOR)- and MAJority (MAJ)- logic functions, and open a large panel of opportunities for future high-performance computing systems. However, main current logic synthesis tools exploit algorithms using NAND/NOR representations that are not able to fully exploit the capabilities of novel XOR- and MAJ-oriented technologies. In this paper, we review some recent work that aims at providing novel logic synthesis techniques that natively assess the logic capabilities of XOR- and MAJ-operators

Boolean Logic Optimization in Majority-Inverter Graphs

We present a Boolean logic optimization framework based on Majority-Inverter Graph (MIG). An MIG is a directed acyclic graph consisting of three-input majority nodes and regular/complemented edges. Current MIG optimization is supported by a consistent algebraic framework. However, when algebraic methods cannot improve a result quality, stronger Boolean methods are needed to attain further optimization. For this purpose, we propose MIG Boolean methods exploiting the error masking property of majority operators. Our MIG Boolean methods insert logic errors that strongly simplify an MIG while being successively masked by the voting nature of majority nodes. Thanks to the datastructure/ methodology fitness, our MIG Boolean methods run in principle as fast as algebraic counterparts. Experiments show that our Boolean methodology combined with state-of-art MIG algebraic techniques enable superior optimization quality. For example, when targeting depth reduction, our MIG optimizer transforms a ripple carry adder into a carry look-ahead one. Considering the set of IWLS’05 (arithmetic intensive) benchmarks, our MIG optimizer reduces by 17.98% (26.69%) the logic network depth while also enhancing size and power activity metrics, with respect to ABC academic optimizer. Without MIG Boolean methods, i.e., using MIG algebraic optimization alone, the previous gains are halved. Employed as front-end to a delay-critical 22-nm ASIC flow (logic synthesis + physical design) our MIG optimizer reduces the average delay/area/power by (15.07%, 4.93%, 1.93%), over 27 academic and industrial benchmarks, as compared to a leading commercial ASIC flow

An Efficient Manipulation Package for Biconditional Binary Decision Diagrams

Biconditional Binary Decision Diagrams (BBDDs) are a novel class of binary decision diagrams where the branching condition, and its associated logic expansion, is biconditional on two variables. Reduced and ordered BBDDs are remarkably compact and unique for a given Boolean function. In order to exploit BBDDs in Electronic Design Automation (EDA) applications, efficient manipulation algorithms must be developed and integrated in a software package. In this paper, we present the theory for efficient BBDD manipulation and its practical software implementation. The key features of the proposed approach are (i) strong canonical form pre-conditioning of stored BBDD nodes, (ii) recursive formulation of Boolean operations in terms of biconditional expansions, (iii) performance-oriented memory management and (iv) dedicated BBDD re-ordering techniques. Experimental results show that the developed BBDD package achieves an average node count reduction of 19.48% and a speed-up factor of 1.63x with respect to a state-of-art decision diagram manipulation package. Employed in the synthesis of datapath circuits, the BBDD manipulation package is capable to advantageously restructure arithmetic operations producing 11.02% smaller and 32.29% faster circuits as compared to a commercial synthesis flow

Majority-based Synthesis for Nanotechnologies

We study the logic synthesis of emerging nanotechnologies whose elementary devices abstraction is a majority voter. We argue that synthesis tools, natively supporting the majority logic abstraction, are the technology enablers. This is because they allow designers to validate majority-based nanotechnologies on large-scale benchmarks. We describe models and data-structures for logic design with majority-based nanotechnologies and we show results of applying new synthesis algorithms and tools. We conclude that new logic synthesis methods are required to achieve a fair assessment on emerging nanotechnologies