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A fundamental problem in circuit complexity is to find explicit functions that require large depth to compute. When considering the natural DeMorgan basis of {OR,AND}, where negations incur no cost, the best known depth lower bounds for an explicit function in NP have the form (3-o(1))log₂ n, established by Håstad (building on others) in the early 1990s. We make progress on the problem of improving this factor of 3, in two different ways: - We consider an "algorithmic method" approach to proving stronger depth lower bounds for non-uniform circuits in the DeMorgan basis. We show that slightly faster algorithms (than what is known) for counting the number of satisfying assignments on subcubic-size DeMorgan formulas would imply supercubic-size DeMorgan formula lower bounds, implying that the depth must be at least (3+ε)log₂ n for some ε > 0. For example, if #SAT on formulas of size n^{2+2ε} can be solved in 2^{n - n^{1-ε}log^k n} time for some ε > 0 and a sufficiently large constant k, then there is a function computable in 2^{O(n)} time with a SAT oracle which does not have n^{3+ε}-size formulas. In fact, the #SAT algorithm only has to work on formulas that are a conjunction of n^{1-ε} subformulas, each of which is n^{1+3ε} size, in order to obtain the supercubic lower bound. As a proof of concept, we show that our new algorithms-to-lower-bounds connection can be applied to prove new lower bounds for "hybrid" DeMorgan formula models which compute interesting functions at their leaves. - Turning to the {NAND} basis, we establish a greater-than-(3 log₂ n) depth lower bound against uniform circuits solving the SAT problem, using an extension of the "indirect diagonalization" method for NAND formulas. Note that circuits over the NAND basis are a special case of circuits over the DeMorgan basis; however, hard functions such as Andreev’s function (known to require depth (3-o(1))log₂ n in the DeMorgan basis) can still be computed with NAND circuits of depth (3+o(1))log₂ n. Our results imply that SAT requires polylogtime-uniform NAND circuits of depth at least 3.603 log₂ n. 

Given the need for ever higher performance, and the failure of CPUs to keep providing single-threaded performance gains, engineers are increasingly turning to highly-parallel custom VLSI chips to implement expensive computations. In VLSI design, the gates and wires of a logical circuit are placed on a 2-dimensional chip with a small number of layers. Traditional VLSI models use gate delay to measure the time complexity of the chip, ignoring the lengths of wires. However, as technology has advanced, wire delay is no longer negligible; it has become an important measure in the design of VLSI chips [Markov, Nature (2014)]. Motivated by this situation, we define and study a model for VLSI chips, called wire-delay VLSI, which takes wire delay into account, going beyond an earlier model of Chazelle and Monier [JACM 1985]. - We prove nearly tight upper bounds and lower bounds (up to logarithmic factors) on the time delay of this chip model for several basic problems. For example, And, Or and Parity require Θ(n^{1/3}) delay, while Addition and Multiplication require ̃ Θ(n^{1/2}) delay, and Triangle Detection on (dense) n-node graphs requires ̃ Θ(n) delay. Interestingly, when we allow input bits to be read twice, the delay for Addition can be improved to Θ(n^{1/3}). - We also show that proving significantly higher lower bounds in our wire-delay VLSI model would imply breakthrough results in circuit lower bounds. Motivated by this barrier, we also study conditional lower bounds on the delay of chips based on the Orthogonal Vectors Hypothesis from fine-grained complexity. 

Abstract We continue the program of proving circuit lower bounds via circuit satisfiability algorithms. So far, this program has yielded several concrete results, proving that functions in$$\mathsf {Quasi}\text {-}\mathsf {NP} = \mathsf {NTIME}[n^{(\log n)^{O(1)}}]$$ $\mathrm{Quasi}-\mathrm{NP}=\mathrm{NTIME}\left[n^{{\left(\log n\right)}^{O\left(1\right)}}\right]$and other complexity classes do not have small circuits (in the worst case and/or on average) from various circuit classes$$\mathcal { C}$$ $C$, by showing that$$\mathcal { C}$$ $C$admits non-trivial satisfiability and/or#SAT algorithms which beat exhaustive search by a minor amount. In this paper, we present a new strong lower bound consequence of having a non-trivial#SAT algorithm for a circuit class$${\mathcal C}$$ $C$. Say that a symmetric Boolean functionf(x₁,…,x_n) issparseif it outputs 1 onO(1) values of$${\sum }_{i} x_{i}$$ ${\sum }_i{x}_i$. We show that for every sparsef, and for all “typical”$$\mathcal { C}$$ $C$, faster#SAT algorithms for$$\mathcal { C}$$ $C$circuits imply lower bounds against the circuit class$$f \circ \mathcal { C}$$ $f\circ C$, which may bestrongerthan$$\mathcal { C}$$ $C$itself. In particular:#SAT algorithms forn^k-size$$\mathcal { C}$$ $C$-circuits running in 2ⁿ/n^ktime (for allk) implyNEXPdoes not have$$(f \circ \mathcal { C})$$ $(f\circ C)$-circuits of polynomial size.#SAT algorithms for$$2^{n^{{\varepsilon }}}$$ $2^{n^{\epsilon }}$-size$$\mathcal { C}$$ $C$-circuits running in$$2^{n-n^{{\varepsilon }}}$$ $2^{n-n^{\epsilon }}$time (for someε> 0) implyQuasi-NPdoes not have$$(f \circ \mathcal { C})$$ $(f\circ C)$-circuits of polynomial size. Applying#SAT algorithms from the literature, one immediate corollary of our results is thatQuasi-NPdoes not haveEMAJ∘ACC⁰∘THRcircuits of polynomial size, whereEMAJis the “exact majority” function, improving previous lower bounds againstACC⁰[Williams JACM’14] andACC⁰∘THR[Williams STOC’14], [Murray-Williams STOC’18]. This is the first nontrivial lower bound against such a circuit class.