This is not a Poisson distribution, but is related. It is a rather interesting distribution, but let me not disclose it at the moment.
First note that, up to the multiple $n^n$, this is the probability generating function for the sum of $n$ iid random variables, which are uniformly distributed on the set $\{1,2,\dots,2^{n-1}\}$.
Dividing by $2^{n-1}$, we get the sum of $n$ iid random variables $\xi_{1,n},\dots,\xi_{n,n}$, uniformly distributed on the set $\{1,2^{-1},2^{-2}\dots,2^{1-n}\}$. Then the characteristic function of the sum $S_n = \xi_{1,n}+\dots+\xi_{n,n}$ is
$$
\varphi_{S_n}(t) = \varphi_{\xi_{1,n}}(t)^n = \left(\frac1n \sum_{k=0}^{n-1}e^{it2^{-k}}\right)^n = \left(1+ \frac1n \sum_{k=0}^{n-1}\big(e^{it2^{-k}}-1\big)\right)^n\\
\to \exp\left\{\sum_{k=0}^{\infty}\big(e^{it2^{-k}}-1\big)\right\} = \prod_{k=0}^{\infty}\exp\left\{e^{it2^{-k}}-1\right\},\quad n\to\infty.
$$
Therefore, $$S_n\overset{w}{\longrightarrow} S = \sum_{k=0}^\infty \frac{\zeta_k}{2^k},\quad n\to\infty,$$
where $\zeta_k$ are iid with Poisson(1) distribution.
Let us study the limit disribution $S$. As @A.S. wrote, its cumulants are $(1-2^{-k})^{-1}$.
Further, let $0.\alpha_1\alpha_2\alpha_3\dots$ be the binary form of fractional part of $S$. Then the sequence $\{\alpha_n,n\ge 1\}$ of digits is stationary (in fact, this is the case for any random variable of the form $\sum_{k=0}^\infty \zeta_k 2^{-k}$ with iid integer-valued variables $\zeta_k$). It is possible to "identify" the marginal distribution of digits, that is, the probability $p = P(\alpha_1 = 1)$. Define for $k\ge 0$
$$
E_k = 1 - 2e^{-1}\sum_{n=0}^\infty \frac{1}{(2^k (2n+1))!}.
$$
Then
$$
p = \frac12\left(1-\prod_{k=0}^\infty E_k\right).
$$
If $p\neq 1/2$, which seems to be the case, then the distribution of $S$ is singular. (In the unlikely case that $p=1/2$, it is still singular since the digits $\alpha_n$ are dependent.) It is also continuous, but at the moment I don't see a simple way to prove this.
Approximate $S_M$ by $S_B=\sum_{i=0}^{n-1} 2^{i}B(n,\frac 1 n)$ or go even further to get $S_P=\sum_{i=0}^{n-1} 2^{i}Pois(1)$. All cumulants of a Poisson are $1$, hence cumulants of $S_P$ are $$\kappa_k=\frac {2^{kn}-1}{2^k-1}$$ yielding variance $\kappa_2\approx \frac{4^n}3$ and $\kappa_{3}\approx\frac {8^n}7$. Quality of approximation of $S_M$ by $S_P$ deteriorate for higher cumulants.
– A.S. Mar 11 '16 at 07:46