Combinational Logic Circuits and Theory of Computation

I'm trying to link Combinational Logic Circuits ( computers based on logical gates only ) with everything I have learned recently in Theory of Computation.

I was wondering whether combinational logic circuits can implement computations in the same way finite state machines can. They seem radically different :

Finite State Machines, however, have a well-defined memory in the form of the states that it can be in. Combinational logic circuits, however, don't have a well-defined memory so to implement algorithms that need memory they kinda use some weird method of serial connection ( see how $C_{out}$ of previous adder is connected to $C_{in}$ of current adder in the image bellow ) .

However radically different might seem, they both seems to be doing computations. For instance, both can implement an algorithm for binary addition ( and even binary multiplication ) however different those implementations might be :

FSM :

Combinational Logic Circuit ( C, as in $C_{in}$ and $C_{out}$, stands for Carry ) :



I'm even thinking ( although still very uncertain ) that we can convert every FSM into a corresponding Combinational Logic Circuit.

So, i'm asking myself :

Can Combinational Logic Circuits also be considered a instantaneous kind of model of computation ? Can we apply all concepts we learn in Computability Theory and Computational Complexity Theory, like space complexity and computability, to it ?

On one hand, it seems like they don't fit as a model of computation because they don't have elementary operations ( like reading/writing of a tape, function reduction, steps on the proof search of logical programming paradigma ), they implement their computations instantaneously.

But on the other hand, they seem to be fit as a model of computation because we can model all kinds of computation with them ( binary addition is one example ), and they can be viewed abstractly ( by only focusing on the truth-tables and the logical gate

Logic circuits are common in complexity theory, where they go by the name circuits. There is a big difference between circuits and models of computation such as the Turing machine: each circuit can only handle inputs of fixed size. In order to fix this, under the circuit computation model, for every input length $n$ there is a circuit $C_n$, and together they compute a function on strings of arbitrary length. This computation model, as stated, is too strong: it can compute uncomputable functions, indeed all functions. The problem is that an infinite sequence of circuits doesn't necessarily have a finite description. In order to fix this problem, we usually demand that the circuits be uniform, that is, that they be generated by some Turing machine, which on input $n$ generates $C_n$.

The circuit model is especially popular in complexity theory, even without the uniformity restriction. The reason is the following observation: a Turing machine running in polynomial time can be converted to a (uniform) sequence of circuits of polynomial size, using essentially the ideas of Cook's theorem (which shows that SAT is NP-complete). Hence if you want to prove that a certain problem cannot be solved in polynomial time, it is enough to show that it cannot be solved by polynomial size circuits.

StackExchange Computer Science Q#37867, answer score: 12