Benefits Of CFC Network Design

One of the biggest evolutions in neural network models in the past few years is the advent of closed-form continuous-time or “CFC” networks.

These networks essentially use a closed form solution for a network model, and gating mechanisms that are pretty fascinating from a design perspective.

In some other posts, I’ve gone into a lot of the engineering that people have put into these models – (Full disclosure: I am affiliated with the liquid AI network program work being done at MIT’s CSAIL lab on this kind of technology.)

However, it’s interesting to look at the actual advantages that these models provide to the engineering community.

Here are three of the major advantages of new CFC models.

Speed and Efficiency

Experts point out that the CFC models are generally faster, both in terms of training, and in the inference capability of the model.

This, they say, is partially because the CFC models don’t require numerical solvers.

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Numerical solvers solve a mathematical problem with formulated input: the numerical solver, according to Wikipedia, “uses a closed form solution to solve a set of ordinary differential equations” in the model.

So what is a closed form solution?

The closed form solution is defined this way at Wolfram: “an equation that solves a given problem in terms of functions and mathematical operations from a given, generally accepted set - a formula that can be evaluated in a finite number of standard operations.” The sourcing further notes that a closed form expression is formed with constants, variables, and a finite set of basic functions connected by “arithmetic operations.”

That provides a little context on the scope of what the design is doing, in terms of algorithm development.

What about this gating capability?

Here’s some of what I heard in a recent talk on CFC networks: “the gating networks are based on sigmoidal functions, modulated by the network’s internal state and input.” All of this starts to sound, in my view, a little like quantum computing, where the basic compute units are more dynamic than static. That is, of course, in a general sense: where qubits function on a superpositioning design, this gating process is completely different. But in either case, it seems like we’re moving beyond binary choices and static, deterministic systems.

Anyway, all of this adds to the power of CFC networks and their appeal for deployments.

Stability and Reliability

We can also talk about some of the advantages of CFC networks in terms of stability and their resistance to some of the problems with traditional networks.

Some of the older conventional network designs can suffer from something called the ‘vanishing ingredient problem.’ Essentially, as the system becomes more top-heavy with network depth, or the length of a sequence or some kind of state complexity, these changes can slow down or even stop training in networks that use backpropagation. CFCs change the game in this regard, making this interesting for engineers contemplating the potential for system convergence.

Scalability

In general, CFC networks also tend to be more scalable, which is good news for enterprises trying to set up business applications.

Think about a business that has a high daily user count, and where this number is quickly growing. The needs are dynamic – so it’s important to have a system that can be easily ramped up to accommodate more traffic.

Having new systems that are versatile in this way is going to help out a lot!

That’s not to say that CFC networks are always the answer. Businesses have to think about whether to deploy a CFC design or other models based on their own criteria – what sort of outputs they need, how big their system is, etc. In some cases, CFC networks work well for real time systems, or for smaller systems that have less compute power, for example.

We’ll go back to this in the future, as we listen to more of our top people talking about using CFC networks in the future. This type of model is taking over quickly, just like with so many other technology changes within the last couple of decades. For example, we’re quickly moving away from the cloud now, and toward edge computing, where, on the other hand, cloud growth was ramping up over a time span of, say, 10 to 5 years ago.

That’s why we keep evaluating these new systems and talking about their applications as they develop. Sometimes it seems like it’s happening in the blink of an eye – and with a view of broader history, in some ways, it is.